{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":102335,"databundleVersionId":12518947,"sourceType":"competition"}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## 1. Import Library\n___","metadata":{}},{"cell_type":"code","source":"import os\nimport gc\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\nimport seaborn as sns \n\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\nfrom colorama import Fore, Style\nc_ = Fore.GREEN\nst_ = Style.BRIGHT\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nprint(f\"{c_}{st_}Import necessary Library!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:35.350354Z","iopub.execute_input":"2025-07-29T14:46:35.351008Z","iopub.status.idle":"2025-07-29T14:46:35.358951Z","shell.execute_reply.started":"2025-07-29T14:46:35.350968Z","shell.execute_reply":"2025-07-29T14:46:35.357955Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip -q install palettable \nimport palettable.colorbrewer.qualitative as pbq","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:35.376381Z","iopub.execute_input":"2025-07-29T14:46:35.376841Z","iopub.status.idle":"2025-07-29T14:46:38.432844Z","shell.execute_reply.started":"2025-07-29T14:46:35.376823Z","shell.execute_reply":"2025-07-29T14:46:38.431973Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 2. Pariticpant Data\n___\n\n| **Column**             | **Description**                                                                                                             |\n| ---------------------- | --------------------------------------------------------------------------------------------------------------------------- |\n| `subject`              | Participant identifier                                                                                                      |\n| `adult_child`          | Indicates whether the participant is a child (0) or an adult (1). Adults are defined as individuals ages 18 years or older. |\n| `age`                  | Participant’s age in years at time of data collection                                                                       |\n| `sex`                  | Participant’s sex assigned at birth, 0 = female, 1 = male                                                                   |\n| `handedness`           | Dominant hand used by the participant, 0 = left-handed, 1 = right-handed                                                    |\n| `height_cm`            | Height of the participant in centimeters                                                                                    |\n| `shoulder_to_wrist_cm` | Distance from shoulder to wrist in centimeters                                                                              |\n| `elbow_to_wrist_cm`    | Distance from elbow to wrist in centimeters                                                                                 |\n","metadata":{}},{"cell_type":"code","source":"train_demo = pd.read_csv('/kaggle/input/cmi-detect-behavior-with-sensor-data/train_demographics.csv')\nprint(f'{c_}Shape of DataFrame: ', train_demo.shape)\nprint(f'{Fore.BLUE}{st_} => Average of Age: {np.mean(train_demo.age):.1f}')\nprint(f'{Fore.BLUE}{st_} => Average of Height(cm): {np.mean(train_demo.height_cm):.1f}cm')\nprint(f'{Fore.BLUE}{st_} => Average of shoulder_to_wrist_cm: {np.mean(train_demo.shoulder_to_wrist_cm):.1f}cm')\nprint(f'{Fore.BLUE}{st_} => Average of elbow_to_wrist_cm: {np.mean(train_demo.elbow_to_wrist_cm):.1f}cm')\nprint(display(train_demo))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.434671Z","iopub.execute_input":"2025-07-29T14:46:38.434911Z","iopub.status.idle":"2025-07-29T14:46:38.454978Z","shell.execute_reply.started":"2025-07-29T14:46:38.434887Z","shell.execute_reply":"2025-07-29T14:46:38.454181Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f'{Fore.BLUE}{st_} => Female Subject: {train_demo[train_demo[\"sex\"] == 0].shape[0]}')\nprint(f'{Fore.BLUE}{st_} => Male Subject: {train_demo[train_demo[\"sex\"] == 1].shape[0]}')\nprint(f'{Fore.BLUE}{st_} => Adult Subject: {train_demo[train_demo[\"adult_child\"] == 1].shape[0]}')\nprint(f'{Fore.BLUE}{st_} => Child Subject: {train_demo[train_demo[\"adult_child\"] == 0].shape[0]}')\n\ntrace1 = go.Pie(\n    labels= [\"Child\", \"Adult\"],\n    values= train_demo['adult_child'].value_counts().sort_index(),\n    hole=0.5,\n    textinfo=\"label+percent\",\n    marker=dict(colors=['rgba(255,0,0,0.5)', 'rgba(0,0,255,0.5)']),\n    showlegend=False,\n)\ntrace2 = go.Pie(\n    labels= [\"Female\", \"Male\"],\n    values= train_demo['sex'].value_counts().sort_index(),\n    hole=0.5,\n    textinfo=\"label+percent\",\n    marker=dict(colors=['rgba(255,0,0,0.5)', 'rgba(0,0,255,0.5)']),\n    showlegend=False,\n)\n\n\nfig = make_subplots(rows=1,cols=2, subplot_titles=(\"Child(0) vs Adult(1)\", \"Female(0) vs Male(1)\"),\n                    specs=[[{'type':'domain'},{'type': 'domain'}]])\nfig.add_trace(trace1, 1, 1)\nfig.add_trace(trace2, 1, 2)\n\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.455727Z","iopub.execute_input":"2025-07-29T14:46:38.456195Z","iopub.status.idle":"2025-07-29T14:46:38.500769Z","shell.execute_reply.started":"2025-07-29T14:46:38.456169Z","shell.execute_reply":"2025-07-29T14:46:38.500199Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f'\\n{Fore.BLUE}{st_}From the bar chart, it’s clear that the proportions of females and males in the child and adult categories are not balanced.')\n\ntrace1 = go.Histogram(\n    y = train_demo[train_demo['sex'] == 0]['adult_child'],\n    name = 'Female',\n    marker=dict(color='rgba(255,0,0,0.5)'),\n    orientation='h',\n)\n\ntrace2 = go.Histogram(\n    y = train_demo[train_demo['sex'] == 1]['adult_child'],\n    name = 'Male',\n    marker=dict(color='rgba(0,0,255,0.5)'),\n    orientation='h',\n)\n\n\nlayout = go.Layout(\n    title = \"Distribution of Child vs Adult\",\n    barmode='group',\n    bargap=0.5,\n    yaxis = dict(title='Child(0) vs Adult(1)', ticklen = 5, zeroline=False),\n    xaxis = dict(title='Count', ticklen = 5, zeroline=False),\n    legend = dict(title='Sex', bordercolor='black', borderwidth=1),\n)\n\nfig = go.Figure(data=[trace1,trace2], layout=layout)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.502123Z","iopub.execute_input":"2025-07-29T14:46:38.502369Z","iopub.status.idle":"2025-07-29T14:46:38.538267Z","shell.execute_reply.started":"2025-07-29T14:46:38.502355Z","shell.execute_reply":"2025-07-29T14:46:38.537615Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f'{Fore.BLUE}{st_} => Min of Age: {np.min(train_demo.age):.1f}')\nprint(f'{Fore.BLUE}{st_} => Max of Age: {np.max(train_demo.age):.1f}')\n\nprint(\"\\nAs shown in the bar chart above, females tend to be concentrated in the child group (under 18), whereas males are more represented in the adult group (18 and older).\")\n\ntrace1 = go.Histogram(\n    x = train_demo[train_demo['sex'] == 0]['age'],\n    name = 'Female',\n    marker=dict(color='rgba(171, 50, 96, 0.6)'),\n    xbins = dict(size=2.5)\n    \n)\n\ntrace2 = go.Histogram(\n    x = train_demo[train_demo['sex'] == 1]['age'],\n    name = 'Male',\n    marker=dict(color='rgba(12, 50, 196, 0.6)'),\n    xbins = dict(size=2.5)\n)\n\nlayout = go.Layout(\n    title = \"Distribution of Age\",\n    barmode='overlay',\n    xaxis = dict(title='Age', ticklen = 5, zeroline=False),\n    yaxis = dict(title='Count', ticklen = 5, zeroline=False),\n    legend = dict(title='Sex', bordercolor='black', borderwidth=1),\n)\n\nfig = go.Figure(data=[trace1,trace2], layout=layout)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.538904Z","iopub.execute_input":"2025-07-29T14:46:38.539090Z","iopub.status.idle":"2025-07-29T14:46:38.578492Z","shell.execute_reply.started":"2025-07-29T14:46:38.539075Z","shell.execute_reply":"2025-07-29T14:46:38.577955Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f'{Fore.BLUE}{st_} => Left-handed Subject: {train_demo[train_demo[\"handedness\"] == 0].shape[0]}')\nprint(f'{Fore.BLUE}{st_} => Right-handed Subject: {train_demo[train_demo[\"handedness\"] == 1].shape[0]}')\n\nprint('\\nAcross age groups, left- and right-handedness appear nearly equal in proportion. When analyzed by sex, the proportion of left-handed individuals stays the same, while that of right-handed individuals differs. This difference seems to stem from the larger number of males compared to females, rather than suggesting any correlation between sex and handedness.')\n\ntrace1 = go.Pie(\n    labels=['Left_Handed','Right_Handed'],\n    values= train_demo['handedness'].value_counts().sort_index(),\n    textinfo=\"label+percent\",\n    hole=0.7,\n    showlegend=False,\n    marker=dict(colors=(\"rgba(255,0,0,0.5)\",\"rgba(0,0,255,0.5)\")),\n)\ntrace2 = go.Histogram(\n    x = train_demo[train_demo['sex']==0]['handedness'],\n    name='Female',\n    marker=dict(color='rgba(255,0,0,0.5)'),\n)\ntrace3 = go.Histogram(\n    x = train_demo[train_demo['sex']==1]['handedness'],\n    name='Male',\n    marker=dict(color='rgba(0,0,255,0.5)'),\n)\n\nfig = make_subplots(rows=1, cols=2,specs=[[{'type':'domain'},{'type':'xy'}]])\n\nfig.add_trace(trace1, 1, 1)\nfig.add_traces([trace2,trace3], 1, 2)\n\nfig.update_layout(\n    title='Distribution of Handedness',\n    barmode='group',\n    bargap=0.5,\n    \n    xaxis1 = dict(title='Left(0) vs Right(1)', ticklen = 5, zeroline=False),\n    yaxis1 = dict(title='Count', ticklen = 5, zeroline=False),\n    legend=dict(title='Sex', bordercolor='black', borderwidth=1),\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.579140Z","iopub.execute_input":"2025-07-29T14:46:38.579358Z","iopub.status.idle":"2025-07-29T14:46:38.633058Z","shell.execute_reply.started":"2025-07-29T14:46:38.579343Z","shell.execute_reply":"2025-07-29T14:46:38.632513Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f'{Fore.BLUE}{st_} => Average of Female Height(cm): {np.mean(train_demo[train_demo[\"sex\"] == 0].height_cm):.1f}cm')\nprint(f'{Fore.BLUE}{st_} => Average of Male Height(cm): {np.mean(train_demo[train_demo[\"sex\"] == 1].height_cm):.1f}cm')\nprint(\"\\nIt’s interesting that the average height of women turned out to be higher than that of men, even though the proportion of children (who tend to be shorter) is much higher among women, while the proportion of adults is higher among men.\")\n\ntrace1 = go.Box(\n    y = train_demo[train_demo['sex'] == 0]['height_cm'],\n    name = \"female\",\n    marker = dict(color='rgba(255,0,0,0.6)'),\n    showlegend=False\n)\ntrace2 = go.Box(\n    y = train_demo[train_demo['sex'] == 1]['height_cm'],\n    name = \"male\",\n    marker = dict(color='rgba(0,0,255,0.6)'),\n    showlegend=False\n)\n\ntrace3 = go.Box(\n    y = train_demo[train_demo['adult_child'] == 0]['height_cm'],\n    name = \"child\",\n    marker = dict(color='rgba(255,0,0,0.6)'),\n    showlegend=False\n)\ntrace4 = go.Box(\n    y = train_demo[train_demo['adult_child'] == 1]['height_cm'],\n    name = \"adult\",\n    marker = dict(color='rgba(0,0,255,0.6)'),\n    showlegend=False\n)\n\nfig = make_subplots(rows=1, cols=2)\n\nfig.add_traces([trace1, trace2], 1, 1)\nfig.add_traces([trace3, trace4], 1, 2)\n\nfig.update_layout(\n    title='Distribution of Height',\n    yaxis = dict(title='Height', ticklen = 5, zeroline=False),\n)\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.633802Z","iopub.execute_input":"2025-07-29T14:46:38.634019Z","iopub.status.idle":"2025-07-29T14:46:38.688562Z","shell.execute_reply.started":"2025-07-29T14:46:38.633994Z","shell.execute_reply":"2025-07-29T14:46:38.687840Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"trace1 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==0]['height_cm'],\n    y = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==0]['shoulder_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Female',\n    marker=dict(color='rgba(255,0,0,0.5)'),\n)\ntrace2 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==1]['height_cm'],\n    y = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==1]['shoulder_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Male',\n    marker=dict(color='rgba(0,0,255,0.5)'),\n)\ntrace3 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==0]['height_cm'],\n    y = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==0]['shoulder_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Female',\n    marker=dict(color='rgba(255,0,0,0.5)'),\n    showlegend=False,\n)\ntrace4 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==1]['height_cm'],\n    y = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==1]['shoulder_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Male',\n    marker=dict(color='rgba(0,0,255,0.5)'),\n    showlegend=False,\n)\n\n\nfig = make_subplots(rows=1, cols=2, subplot_titles=(\"Child\", \"Adult\"))\n\nfig.add_traces([trace1,trace2], 1, 1)\nfig.add_traces([trace3,trace4], 1, 2)\n\n\nfig.update_layout(\n    title=dict(\n    text=\"<b>Correlation with Height and Shoulder_to_Wrist</b>\",\n    font=dict(size=20, family=\"Arial\", color=\"black\"),),\n    xaxis1 = dict(title='Height', ticklen = 5, zeroline=False),\n    yaxis1 = dict(title='Shoulder_to_Wrist', ticklen = 5, zeroline=False),\n    xaxis2 = dict(title='Height', ticklen = 5, zeroline=False),\n    yaxis2 = dict(title='Shoulder_to_Wrist', ticklen = 5, zeroline=False),\n    legend=dict(title='Sex', bordercolor='black', borderwidth=1)\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.689516Z","iopub.execute_input":"2025-07-29T14:46:38.689862Z","iopub.status.idle":"2025-07-29T14:46:38.754399Z","shell.execute_reply.started":"2025-07-29T14:46:38.689837Z","shell.execute_reply":"2025-07-29T14:46:38.753824Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"trace1 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==0]['height_cm'],\n    y = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==0]['elbow_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Female',\n    marker=dict(color='rgba(255,0,0,0.5)'),\n)\ntrace2 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==1]['height_cm'],\n    y = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==1]['elbow_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Male',\n    marker=dict(color='rgba(0,0,255,0.5)'),\n)\ntrace3 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==0]['height_cm'],\n    y = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==0]['elbow_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Female',\n    marker=dict(color='rgba(255,0,0,0.5)'),\n    showlegend=False,\n)\ntrace4 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==1]['height_cm'],\n    y = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==1]['elbow_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Male',\n    marker=dict(color='rgba(0,0,255,0.5)'),\n    showlegend=False,\n)\n\n\nfig = make_subplots(rows=1, cols=2, subplot_titles=(\"Child\", \"Adult\"))\n\nfig.add_traces([trace1,trace2], 1, 1)\nfig.add_traces([trace3,trace4], 1, 2)\n\n\nfig.update_layout(\n    title=dict(\n    text=\"<b>Correlation with Height and Elbow_to_Wrist</b>\",\n    font=dict(size=20, family=\"Arial\", color=\"black\"),),\n    xaxis1 = dict(title='Height', ticklen = 5, zeroline=False),\n    yaxis1 = dict(title='Elbow_to_Wrist', ticklen = 5, zeroline=False),\n    xaxis2 = dict(title='Height', ticklen = 5, zeroline=False),\n    yaxis2 = dict(title='Elbow_to_Wrist', ticklen = 5, zeroline=False),\n    legend=dict(title='Sex', bordercolor='black', borderwidth=1)\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.755277Z","iopub.execute_input":"2025-07-29T14:46:38.755578Z","iopub.status.idle":"2025-07-29T14:46:38.820079Z","shell.execute_reply.started":"2025-07-29T14:46:38.755553Z","shell.execute_reply":"2025-07-29T14:46:38.819523Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"trace1 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==0]['shoulder_to_wrist_cm'],\n    y = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==0]['elbow_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Female',\n    marker=dict(color='rgba(255,0,0,0.5)'),\n)\ntrace2 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==1]['shoulder_to_wrist_cm'],\n    y = train_demo[train_demo['adult_child'] == 0][train_demo['sex']==1]['elbow_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Male',\n    marker=dict(color='rgba(0,0,255,0.5)'),\n)\ntrace3 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==0]['shoulder_to_wrist_cm'],\n    y = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==0]['elbow_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Female',\n    marker=dict(color='rgba(255,0,0,0.5)'),\n    showlegend=False,\n)\ntrace4 = go.Scatter(\n    x = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==1]['shoulder_to_wrist_cm'],\n    y = train_demo[train_demo['adult_child'] == 1][train_demo['sex']==1]['elbow_to_wrist_cm'],\n    mode = 'markers',\n    name = 'Male',\n    marker=dict(color='rgba(0,0,255,0.5)'),\n    showlegend=False,\n)\n\n\nfig = make_subplots(rows=1, cols=2, subplot_titles=(\"Child\", \"Adult\"))\n\nfig.add_traces([trace1,trace2], 1, 1)\nfig.add_traces([trace3,trace4], 1, 2)\n\n\nfig.update_layout(\n    title=dict(\n    text=\"<b>Correlation with Shoulder_to_Wrist and Elbow_to_Wrist</b>\",\n    font=dict(size=20, family=\"Arial\", color=\"black\"),),\n    xaxis1 = dict(title='Shoulder_to_Wrist', ticklen = 5, zeroline=False),\n    yaxis1 = dict(title='Elbow_to_Wrist', ticklen = 5, zeroline=False),\n    xaxis2 = dict(title='Shoulder_to_Wrist', ticklen = 5, zeroline=False),\n    yaxis2 = dict(title='Elbow_to_Wrist', ticklen = 5, zeroline=False),\n    legend=dict(title='Sex', bordercolor='black', borderwidth=1)\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.823022Z","iopub.execute_input":"2025-07-29T14:46:38.823216Z","iopub.status.idle":"2025-07-29T14:46:38.893744Z","shell.execute_reply.started":"2025-07-29T14:46:38.823201Z","shell.execute_reply":"2025-07-29T14:46:38.893194Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 3. Sensor Data\n___","metadata":{}},{"cell_type":"markdown","source":"| **Column**                         | **Description**                                                                                                                          |\n| ---------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |\n| `row_id`                           | Unique row ID                                                                                                                            |\n| `sequence_id`                      | An ID for the batch of sensor data. Each sequence includes one **Transition**, one **Pause**, and one **Gesture**                        |\n| `sequence_type`                    | If the gesture is a **target** or **non-target** type. (Train only)                                                                      |\n| `sequence_counter`                 | A counter of the row within each sequence                                                                                                |\n| `subject`                          | A unique ID for the subject who provided the data                                                                                        |\n| `gesture`                          | The target column. Description of the **Gesture** sequence (Train only)                                                                  |\n| `orientation`                      | Description of the subject’s orientation during the sequence <br> e.g., *sitting*, *sitting lean forward*, *lying back*, *lying on side* |\n| `behavior`                         | A description of the subject’s behavior during the current phase of the sequence                                                         |\n| `acc_x`, `acc_y`, `acc_z`          | Linear acceleration along three axes (in m/s²) measured by the IMU sensor                                                                |\n| `rot_w`, `rot_x`, `rot_y`, `rot_z` | Orientation data combining IMU gyroscope, accelerometer, and magnetometer to describe 3D orientation                                     |\n| `thm_1` \\~ `thm_5`                 | Temperatures (°C) recorded by five thermopile sensors on the watch. Sensor indices correspond to the overview photo                      |\n| `tof_1_v0` \\~ `tof_5_v63`          | Time-of-flight (ToF) sensor readings. Each of the 5 ToF sensors provides a 64-pixel grid (from `v0` to `v63`) measuring distance         |\n","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/cmi-detect-behavior-with-sensor-data/train.csv')\nprint(f\"{c_}Shape of DataFrame: \", train_df.shape)\nprint(f\"\\n{Fore.BLUE}{st_}There are total {train_df['sequence_id'].nunique()} seqeunces\")\nprint(f\"{Fore.BLUE}{st_}There are total {train_df['subject'].nunique()} subjects\")\nprint(f\"{Fore.BLUE}{st_}On average Each Subject did {train_df['sequence_id'].nunique()/train_df['subject'].nunique():.1f} Gesture Experiments\")\nprint(display(train_df))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:38.894359Z","iopub.execute_input":"2025-07-29T14:46:38.894570Z","iopub.status.idle":"2025-07-29T14:46:55.575666Z","shell.execute_reply.started":"2025-07-29T14:46:38.894554Z","shell.execute_reply":"2025-07-29T14:46:55.574943Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"\\n{Fore.BLUE}{st_}There are total {train_df[train_df['sequence_type'] == 'Target']['gesture'].nunique()} Gesture Type in Target\")\nprint(f\"{Fore.BLUE}{st_}There are total {train_df[train_df['sequence_type'] == 'Non-Target']['gesture'].nunique()} Gesture Type in Non-Target\")\nprint(\"\\nAs you know, our task is twofold: first, to distinguish BFRB-like gestures from non-BFRB-like gestures, which are distributed in a 6:4 ratio; and second, to further classify the specific type of BFRB-like gesture.However, as shown in the chart below, each of the eight gesture types within the Target class accounts for exactly 12.5%, reflecting an even distribution across all gesture categories.\")\n\n\ntmp1 = train_df.groupby(['sequence_id']).agg('first')['sequence_type'].value_counts()\ntmp2 = train_df[train_df['sequence_type'] == 'Target'].groupby(['sequence_id']).agg('first')['gesture'].value_counts()\ntmp3 = train_df[train_df['sequence_type'] == 'Non-Target'].groupby(['sequence_id']).agg('first')['gesture'].value_counts()\n\ntrace1 = go.Pie(\n    labels = tmp1.index,\n    values = tmp1,\n    hole = 0.7,\n    textinfo = \"label+percent\",\n    showlegend=False\n)\n\ntrace2 = go.Pie(\n    labels = tmp2.index,\n    values = tmp2,\n    hole = 0.7,\n    textinfo = \"percent\",\n    showlegend=False\n)\ntrace3 = go.Pie(\n    labels = tmp3.index,\n    values = tmp3,\n    hole = 0.7,\n    textinfo = \"percent\",\n    showlegend=False\n)\n\n\nfig = make_subplots(rows=1, cols=3, subplot_titles=(\"Target vs Non-Target\", \"Gesture in Target\", \"Gesture in Non-Target\"),specs=[[{'type': 'domain'}, {'type': 'domain'},{'type': 'domain'}]])\n\nfig.add_trace(trace1, 1,1)\nfig.add_trace(trace2, 1,2)\nfig.add_trace(trace3, 1,3)\n\nfig.update_layout(\n    title=\"Distribution of Target\",\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:46:55.576337Z","iopub.execute_input":"2025-07-29T14:46:55.576593Z","iopub.status.idle":"2025-07-29T14:47:00.206927Z","shell.execute_reply.started":"2025-07-29T14:46:55.576575Z","shell.execute_reply":"2025-07-29T14:47:00.206343Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"tmp = train_df.groupby(['subject','sequence_id'])['gesture'].agg(\"first\").reset_index()\ntmp2 = tmp.groupby(['subject'])['gesture'].value_counts().reset_index()\ntmp3 = tmp2.groupby(['gesture'])['count'].agg('mean').reset_index()\n\ntrace = go.Bar(\n    x = tmp3['gesture'],\n    y = tmp3['count'],\n    marker = dict(color = f'rgba(0,0,255,0.2)',\n                 line=dict(color=f'rgb(255,255,0)', width=2)),\n)\n\nlayout = go.Layout(\n    title=\"The Average Gesture Count per Subject\"\n)\n\nfig = go.Figure(data=trace, layout=layout)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:00.207718Z","iopub.execute_input":"2025-07-29T14:47:00.207990Z","iopub.status.idle":"2025-07-29T14:47:00.347873Z","shell.execute_reply.started":"2025-07-29T14:47:00.207965Z","shell.execute_reply":"2025-07-29T14:47:00.347249Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"tmp1 = train_df.groupby(['subject','sequence_id']).agg('max').reset_index()\ntmp2 = train_df.groupby('subject')['sequence_id'].nunique().reset_index()\n\nprint(f\"\\n{Fore.BLUE}{st_}The Average of Sequence Lengths is {np.mean(tmp1['sequence_counter']):.2f}\")\nprint(f\"\\n{Fore.BLUE}{st_}The Average of Number of Sequnces per subject is {np.mean(tmp2['sequence_id']):.2f}\")\n\ntrace1 = go.Histogram(\n    x = tmp1['sequence_counter'],\n    opacity=0.75,\n    showlegend=False,\n) \n\ntrace2 = go.Histogram(\n    x = tmp2['sequence_id'],\n    opacity=0.75,\n    showlegend=False,\n) \n\nfig = make_subplots(1,2, subplot_titles=(\"Distribution of Sequence Lengths\", 'Distribution of Number of Sequences per Subject'))\n\nfig.add_trace(trace1, 1, 1)\nfig.add_trace(trace2, 1, 2)\n\nfig.update_layout(\n    xaxis1 = dict(title='Sequence Length (frames)', ticklen = 5, zeroline=False),\n    yaxis1 = dict(title='Count', ticklen = 5, zeroline=False),\n    xaxis2 = dict(title='Number of Sequences', ticklen = 5, zeroline=False),\n    yaxis2 = dict(title='Count', ticklen = 5, zeroline=False),\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:00.348587Z","iopub.execute_input":"2025-07-29T14:47:00.348848Z","iopub.status.idle":"2025-07-29T14:47:04.279080Z","shell.execute_reply.started":"2025-07-29T14:47:00.348829Z","shell.execute_reply":"2025-07-29T14:47:04.278500Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"✅ {Fore.BLUE}{st_}There are more outliers above 300 in the Target sequences than in the Non-Target sequences.\\n\")\nprint(f\"✅ {Fore.BLUE}{st_}Looking first at the gestures within the Target category, the Neck-scratch gesture stands out, exhibiting sequence counts exceeding 300 — a pattern not observed in other gestures. In contrast, within the Non-Target category, the gestures Text on phone and Write name in air are notable.\")\n\n\nfig = make_subplots(rows=1, cols=3, subplot_titles=(\"Target vs Non-Target\", \"Gesture in Target\", \"Gesture in Non-Target\"))\n\nfor seq_type, color in zip(['Target', 'Non-Target'], ['red', 'blue']):\n    fig.add_trace(\n        go.Box(\n            y=train_df.loc[train_df['sequence_type'] == seq_type, 'sequence_counter'],\n            name=seq_type,\n            marker_color=color,\n            boxmean='sd',\n            showlegend=False,\n        ), 1, 1\n    )\n\n\nfor ges_type in train_df[train_df['sequence_type'] == 'Target']['gesture'].unique():\n    fig.add_trace(\n        go.Box(\n            y=train_df.loc[train_df['gesture'] == ges_type, 'sequence_counter'],\n            name=ges_type,\n            boxmean='sd',\n            showlegend=False,\n        ), 1, 2\n    )\n\nfor ges_type in train_df[train_df['sequence_type'] == 'Non-Target']['gesture'].unique():\n    fig.add_trace(\n        go.Box(\n            y=train_df.loc[train_df['gesture'] == ges_type, 'sequence_counter'],\n            name=ges_type,\n            boxmean='sd',\n            showlegend=False,\n        ), 1, 3\n    )\n\nfig.update_layout(\n    title=\"Correlation with Gesture and Sequence Counter\",\n    yaxis = dict(title='Number of Sequences', ticklen = 5, zeroline=False),\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:04.279808Z","iopub.execute_input":"2025-07-29T14:47:04.280094Z","iopub.status.idle":"2025-07-29T14:47:05.876314Z","shell.execute_reply.started":"2025-07-29T14:47:04.280075Z","shell.execute_reply":"2025-07-29T14:47:05.875689Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig = make_subplots(\n    rows=1, cols=2,\n    subplot_titles=(\"Orientation in Target Gesture\", \"Orientation in Non-Target Gesture\")\n)\n\ncolors = ['#636EFA', '#EF553B', '#00CC96', '#AB63FA']\n\ntmp2 = tmp1[tmp1['sequence_type'] == 'Target']\nfor orien, col in zip(tmp2['orientation'].unique(), colors):\n    counts = tmp2[tmp2['orientation'] == orien]['gesture'].value_counts()\n    fig.add_trace(\n        go.Bar(\n            x=counts.index,\n            y=counts.values,\n            marker_color=col,\n            name=f\"{orien}\",\n        ),\n        row=1, col=1\n    )\n\n\ntmp3 = tmp1[tmp1['sequence_type'] == 'Non-Target']\nfor orien, col in zip(tmp3['orientation'].unique(),colors):\n    counts = tmp3[tmp3['orientation'] == orien]['gesture'].value_counts()\n    fig.add_trace(\n        go.Bar(\n            x=counts.index,\n            y=counts.values,\n            marker_color = col,\n            showlegend=False\n        ),\n        row=1, col=2\n    )\n\nfig.update_layout(\n    barmode='group',\n    xaxis1 = dict(title='Gestures', ticklen = 5, zeroline=False),\n    yaxis1 = dict(title='Count', ticklen = 5, zeroline=False),\n    xaxis2 = dict(title='Gestures', ticklen = 5, zeroline=False),\n    yaxis2 = dict(title='Count', ticklen = 5, zeroline=False),\n)\n\nfig.show(renderer='iframe')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:05.877099Z","iopub.execute_input":"2025-07-29T14:47:05.877364Z","iopub.status.idle":"2025-07-29T14:47:05.956515Z","shell.execute_reply.started":"2025-07-29T14:47:05.877342Z","shell.execute_reply":"2025-07-29T14:47:05.955994Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.groupby(['subject','sequence_id'])['behavior'].nunique().value_counts()\n\n## ✅ Every Sequence_id has 3 unique behavior including perform gesture\n## ✅ But Sequence_id(SEQ_011975) has only 2 unique behavior and not incldue perform gesture\n## ✅ So When training Dataset, I'll drop out SEQ_011975","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:05.957534Z","iopub.execute_input":"2025-07-29T14:47:05.958007Z","iopub.status.idle":"2025-07-29T14:47:06.068549Z","shell.execute_reply.started":"2025-07-29T14:47:05.957988Z","shell.execute_reply":"2025-07-29T14:47:06.067967Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df = train_df[train_df['sequence_id'] != 'SEQ_011975'].reset_index(drop=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:06.069239Z","iopub.execute_input":"2025-07-29T14:47:06.069502Z","iopub.status.idle":"2025-07-29T14:47:07.224089Z","shell.execute_reply.started":"2025-07-29T14:47:06.069461Z","shell.execute_reply":"2025-07-29T14:47:07.223512Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df['behavior'].value_counts()\n\n## ✅ There are 4 Behavior(Relaxes and moves hand to target location, moves hand to target location, hand at target location, performs gesture)\n## ✅ But Every Sequence only use 3 Behavior \n## ✅ First Method: Relaxes and moves hand to target loaction -> hand at target location -> performs gesture\n## ✅ Second Mtehod: Moves hand to target location -> hand at target location -> performs gesture","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:07.224795Z","iopub.execute_input":"2025-07-29T14:47:07.225428Z","iopub.status.idle":"2025-07-29T14:47:07.267462Z","shell.execute_reply.started":"2025-07-29T14:47:07.225409Z","shell.execute_reply":"2025-07-29T14:47:07.266925Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"trace1 = go.Pie(\n    labels= train_df['behavior'].value_counts().index,\n    values= train_df['behavior'].value_counts(),\n    hole=0.7,\n    textinfo=\"label+percent\",\n    showlegend = False\n)\n\ntmp = train_df[train_df['sequence_id'] != 'SEQ_011975']\n\ntrace2 = go.Pie(\n    labels= tmp.groupby(['subject','sequence_id'])['behavior'].unique().apply(tuple).value_counts().index,\n    values= tmp.groupby(['subject','sequence_id'])['behavior'].unique().apply(tuple).value_counts(),\n    hole=0.7,\n    textinfo=\"percent\",\n    showlegend=False,\n) \n\n\nfig = make_subplots(rows=1, cols=2, subplot_titles=(\"Behavior Types\", \"Behavior Flow in each sequence\"), specs=[[{'type': 'domain'},{'type': 'domain'}]])\n\nfig.add_trace(trace1, 1, 1)\nfig.add_trace(trace2, 1, 2)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:07.268233Z","iopub.execute_input":"2025-07-29T14:47:07.268507Z","iopub.status.idle":"2025-07-29T14:47:08.753028Z","shell.execute_reply.started":"2025-07-29T14:47:07.268490Z","shell.execute_reply":"2025-07-29T14:47:08.752401Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f'\\n✅{Fore.BLUE}{st_} As shown in the graph, each gesture exhibits two behavior flows occurring in approximately equal proportions, roughly 50-50. This indicates that the competition organizers have artificially balanced the dataset to ensure an equal distribution of behavior flows for each gesture.')\nprint(\"\\n✅ On average, each subject performs around 100 gesture measurements. Given this, it is highly likely that each identical gesture was executed with two different behavior flows at least twice.\")\nprint(\"\\n✅ Considering the two behavior types, four orientations, and eighteen gestures, the total combinations would be 2 × 4 × 18 = 144. However, since the ten Non-Target orientations have fewer than four orientations, it is likely that the average number of measurements per subject is around 100.\")\n\ntmp = train_df.groupby(['subject','sequence_id']).agg('max').reset_index().drop(columns='behavior')\ntmp2 = train_df.groupby(['subject','sequence_id'])['behavior'].unique().apply(lambda x: \" -> \".join(x)).reset_index()[['sequence_id','behavior']]\ntmp1 = tmp.merge(tmp2, on='sequence_id', how='left')\n\nfig = make_subplots(\n    rows=1, cols=2,\n    subplot_titles=(\"Behavior flow in Target Gesture\", \"Behavior flow in Non-Target Gesture\")\n)\n\ncolors = ['#636EFA', '#EF553B']\n\ntmp2 = tmp1[tmp1['sequence_type'] == 'Target']\nfor i, (bh, col) in enumerate(zip(tmp2['behavior'].unique(), colors)):\n    counts = tmp2[tmp2['behavior'] == bh]['gesture'].value_counts()\n    fig.add_trace(\n        go.Bar(\n            x=counts.index,\n            y=counts.values,\n            marker_color=col,\n            name=f\"FLOW_{i+1}\",\n        ),\n        row=1, col=1\n    )\n\n\ntmp3 = tmp1[tmp1['sequence_type'] == 'Non-Target']\nfor bh, col in zip(tmp3['behavior'].unique(),colors):\n    counts = tmp3[tmp3['behavior'] == bh]['gesture'].value_counts()\n    fig.add_trace(\n        go.Bar(\n            x=counts.index,\n            y=counts.values,\n            marker_color = col,\n            showlegend=False\n        ),\n        row=1, col=2\n    )\n\nfig.update_layout(\n    barmode='group',\n    xaxis1 = dict(title='Gestures', ticklen = 5, zeroline=False),\n    yaxis1 = dict(title='Count', ticklen = 5, zeroline=False),\n    xaxis2 = dict(title='Gestures', ticklen = 5, zeroline=False),\n    yaxis2 = dict(title='Count', ticklen = 5, zeroline=False),\n)\n\nfig.show(renderer='iframe')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:08.753697Z","iopub.execute_input":"2025-07-29T14:47:08.753872Z","iopub.status.idle":"2025-07-29T14:47:13.090429Z","shell.execute_reply.started":"2025-07-29T14:47:08.753857Z","shell.execute_reply":"2025-07-29T14:47:13.089707Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"tmp = train_df.groupby(['subject','sequence_id'])['behavior'].value_counts().unstack().reset_index().fillna(0)\ntmp['seqeunce_length'] = tmp['Hand at target location'] + tmp['Moves hand to target location'] + tmp['Performs gesture'] + tmp['Relaxes and moves hand to target location']\ntmp['Hand at target location Ratio'] = tmp['Hand at target location']/tmp['seqeunce_length'] * 100\ntmp['Moves hand to target location Ratio'] = tmp['Moves hand to target location']/tmp['seqeunce_length'] * 100\ntmp['Performs gesture Ratio'] = tmp['Performs gesture']/tmp['seqeunce_length'] * 100\ntmp['Relaxes and moves hand to target location Ratio'] = tmp['Relaxes and moves hand to target location']/tmp['seqeunce_length'] * 100\n\ntrain_df = train_df.merge(tmp[tmp.columns[1:]], on='sequence_id', how='left')\ntrain_df['sequence_counter_pct'] = train_df['sequence_counter'] / train_df['seqeunce_length'] * 100\n\ntmp = train_df.groupby(['subject','sequence_id'])['behavior'].unique().apply(lambda x: \" -> \".join(x)).reset_index().rename(columns={'behavior': 'behavior_flow'})\ntrain_df = train_df.merge(tmp[tmp.columns[1:]], on='sequence_id', how='left')\n\n\nFLOW_1 = 'Relaxes and moves hand to target location -> Hand at target location -> Performs gesture'\nFLOW_2 = 'Moves hand to target location -> Hand at target location -> Performs gesture'\n\nprint(f\"{Fore.GREEN}{st_}### Flow: {FLOW_1} ###\")\nprint(f\"\\n{Fore.BLUE}{st_}✅ The Average Percentage of Relaxes and moves hand to target location in Seqeunce: {np.mean(train_df[train_df['behavior_flow'] == FLOW_1]['Relaxes and moves hand to target location Ratio']):.1f}\")\nprint(f\"\\n{Fore.BLUE}{st_}✅ The Average Percentage of Hand at target location in Seqeunce: {np.mean(train_df[train_df['behavior_flow'] == FLOW_1]['Hand at target location Ratio']):.1f}\")\nprint(f\"\\n{Fore.BLUE}{st_}✅ The Average Percentage of Performs gesture in Seqeunce: {np.mean(train_df[train_df['behavior_flow'] == FLOW_1]['Performs gesture Ratio']):.1f}\")\n\nprint(f\"\\n{Fore.GREEN}{st_}### Flow: {FLOW_2} ###\")\nprint(f\"\\n{Fore.BLUE}{st_}✅ The Average Percentage of Moves hand to target location in Seqeunce: {np.mean(train_df[train_df['behavior_flow'] == FLOW_2]['Moves hand to target location Ratio']):.1f}\")\nprint(f\"\\n{Fore.BLUE}{st_}✅ The Average Percentage of Hand at target location in Seqeunce: {np.mean(train_df[train_df['behavior_flow'] == FLOW_2]['Hand at target location Ratio']):.1f}\")\nprint(f\"\\n{Fore.BLUE}{st_}✅ The Average Percentage of Performs gesture in Seqeunce: {np.mean(train_df[train_df['behavior_flow'] == FLOW_2]['Performs gesture Ratio']):.1f}\")\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:13.091317Z","iopub.execute_input":"2025-07-29T14:47:13.091610Z","iopub.status.idle":"2025-07-29T14:47:18.234379Z","shell.execute_reply.started":"2025-07-29T14:47:13.091582Z","shell.execute_reply":"2025-07-29T14:47:18.233507Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nprint(f\"\\n{Fore.GREEN}{st_}### Flow1: {FLOW_1} ###\")\nprint(f\"\\n{Fore.GREEN}{st_}### Flow2: {FLOW_2} ###\")\n\nfig = make_subplots(1, 2, subplot_titles=(\"Flow 1\", \"Flow 2\"))\n\ntmp1 = train_df.loc[(train_df['behavior_flow'] == FLOW_1) & (train_df['sequence_type'] == 'Target')]\n\nfor bh in tmp1['behavior'].unique():\n    sub_df = tmp1[tmp1['behavior'] == bh].groupby('gesture')[f'{bh} Ratio'].mean().reset_index()\n\n    fig.add_trace(\n        go.Bar(\n            y=sub_df['gesture'],\n            x=sub_df[f'{bh} Ratio'],\n            name=bh,\n            orientation='h',\n            showlegend=False,\n        ), 1, 1\n    )\n\ntmp2 = train_df.loc[(train_df['behavior_flow'] == FLOW_2) & (train_df['sequence_type'] == 'Target')]\n\nfor bh in tmp2['behavior'].unique():\n    sub_df = tmp2[tmp2['behavior'] == bh].groupby('gesture')[f'{bh} Ratio'].mean().reset_index()\n\n    fig.add_trace(\n        go.Bar(\n            y=sub_df['gesture'],\n            x=sub_df[f'{bh} Ratio'],\n            name=bh,\n            orientation='h',\n            showlegend=False,\n        ), 1, 2\n    )\n\nfig.update_layout(\n    title=\"Distribution of Sequence Counter per Behavior in Target\",\n    barmode='stack',\n)\n\nfor annotation in fig.layout.annotations:\n    annotation.font.size = 12\n\nfig.show(renderer='iframe')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:18.235368Z","iopub.execute_input":"2025-07-29T14:47:18.235860Z","iopub.status.idle":"2025-07-29T14:47:19.412316Z","shell.execute_reply.started":"2025-07-29T14:47:18.235835Z","shell.execute_reply":"2025-07-29T14:47:19.411602Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nprint(f\"\\n{Fore.GREEN}{st_}### Flow1: {FLOW_1} ###\")\nprint(f\"\\n{Fore.GREEN}{st_}### Flow2: {FLOW_2} ###\")\n\nfig = make_subplots(1, 2, subplot_titles=(\"Flow 1\", \"Flow 2\"))\n\ntmp1 = train_df.loc[(train_df['behavior_flow'] == FLOW_1) & (train_df['sequence_type'] != 'Target')]\n\nfor bh in tmp1['behavior'].unique():\n    sub_df = tmp1[tmp1['behavior'] == bh].groupby('gesture')['sequence_counter_pct'].mean().reset_index()\n\n    fig.add_trace(\n        go.Bar(\n            y=sub_df['gesture'],\n            x=sub_df['sequence_counter_pct'],\n            name=bh,\n            orientation='h',\n            showlegend=False,\n        ), 1, 1\n    )\n\ntmp2 = train_df.loc[(train_df['behavior_flow'] == FLOW_2) & (train_df['sequence_type'] != 'Target')]\n\nfor bh in tmp2['behavior'].unique():\n    sub_df = tmp2[tmp2['behavior'] == bh].groupby('gesture')['sequence_counter_pct'].mean().reset_index()\n\n    fig.add_trace(\n        go.Bar(\n            y=sub_df['gesture'],\n            x=sub_df['sequence_counter_pct'],\n            name=bh,\n            orientation='h',\n            showlegend=False,\n        ), 1, 2\n    )\n\nfig.update_layout(\n    title=\"Distribution of Sequence Counter per Behavior in Non-Target\",\n    barmode='stack',\n)\n\nfor annotation in fig.layout.annotations:\n    annotation.font.size = 12\n\nfig.show(renderer='iframe')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:19.413178Z","iopub.execute_input":"2025-07-29T14:47:19.413502Z","iopub.status.idle":"2025-07-29T14:47:20.274160Z","shell.execute_reply.started":"2025-07-29T14:47:19.413449Z","shell.execute_reply":"2025-07-29T14:47:20.273527Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df['phase'].value_counts()\n\n## ✅ Transition: Relaxes and moves hand to target location, Moves hand to target location, Hand at target location\n## ✅ Gesture: Performs gesture\n## We'll use two information(Transition, Gesture) when training model.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:20.274877Z","iopub.execute_input":"2025-07-29T14:47:20.275146Z","iopub.status.idle":"2025-07-29T14:47:20.318234Z","shell.execute_reply.started":"2025-07-29T14:47:20.275122Z","shell.execute_reply":"2025-07-29T14:47:20.317684Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"tmp = train_df.groupby(['subject','sequence_id'])['phase'].value_counts().unstack().reset_index()\ntmp['sum'] = tmp['Gesture'] + tmp['Transition']\ntmp['Gesture_Ratio'] = tmp['Gesture']/tmp['sum'] * 100\ntmp['Transition_Ratio'] = tmp['Transition']/tmp['sum'] * 100\n\ntrain_df = train_df.merge(tmp[['sequence_id','Gesture','Transition','Gesture_Ratio','Transition_Ratio']], on='sequence_id', how='left')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:20.318963Z","iopub.execute_input":"2025-07-29T14:47:20.319438Z","iopub.status.idle":"2025-07-29T14:47:21.159222Z","shell.execute_reply.started":"2025-07-29T14:47:20.319414Z","shell.execute_reply":"2025-07-29T14:47:21.158394Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"\\n{Fore.BLUE}{st_} The Average Gesture Ratio in Sequence: {np.mean(train_df['Gesture_Ratio']):.1f}\")\nprint(f\"\\n{Fore.BLUE}{st_} The Average Transition Ratio in Sequence: {np.mean(train_df['Transition_Ratio']):.1f}\")\n\ntrace1 = go.Histogram(\n    x = train_df['Gesture_Ratio'],\n    opacity=0.5,\n    marker_color='red',\n    name = 'Gesture Ratio',\n)\n\ntrace2 = go.Histogram(\n    x = train_df['Transition_Ratio'],\n    opacity=0.5,\n    marker_color='blue',\n    name = 'Transition Ratio',\n)\n\nfig = go.Figure()\n\nfig.add_traces([trace1, trace2])\n\nfig.update_layout(\n    barmode='overlay',\n    xaxis_title=\"Percent\",\n)\n\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:21.160005Z","iopub.execute_input":"2025-07-29T14:47:21.160225Z","iopub.status.idle":"2025-07-29T14:47:21.312217Z","shell.execute_reply.started":"2025-07-29T14:47:21.160208Z","shell.execute_reply":"2025-07-29T14:47:21.311499Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Let's See Too Many Gesture or Transition","metadata":{}},{"cell_type":"code","source":"tmp = train_df.groupby(['subject','sequence_id']).agg('first').reset_index()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:21.317064Z","iopub.execute_input":"2025-07-29T14:47:21.317264Z","iopub.status.idle":"2025-07-29T14:47:23.033333Z","shell.execute_reply.started":"2025-07-29T14:47:21.317249Z","shell.execute_reply":"2025-07-29T14:47:23.032514Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig = make_subplots(1,2)\n\n\nfig.add_trace(\n        go.Box(\n            x=tmp['gesture'],\n            y=tmp['Transition_Ratio'],\n            name='Transition_Ratio',\n            boxmean='sd'\n        ), 1, 1\n    )\nfig.add_trace(\n        go.Box(\n            x=tmp['gesture'],\n            y=tmp['Gesture_Ratio'],\n            name='Gesture Ratio',\n            boxmean='sd'\n        ), 1, 2\n    )\n\nfig.add_hrect(\n    y0=95,\n    y1=100,\n    fillcolor=\"red\",\n    opacity=0.2,\n    layer=\"below\",\n    line_width=0,\n    row=1, col=1\n)\n\nfig.add_hrect(\n    y0=85,\n    y1=100,\n    fillcolor=\"red\",\n    opacity=0.2,\n    layer=\"below\",\n    line_width=0,\n    row=1, col=2\n)\n\nfig.update_layout(\n    title= \"Distribution of Phase Ratio\",\n    boxmode='group' \n)\n\nfig.show(renderer='iframe')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:23.034195Z","iopub.execute_input":"2025-07-29T14:47:23.034435Z","iopub.status.idle":"2025-07-29T14:47:23.098462Z","shell.execute_reply.started":"2025-07-29T14:47:23.034408Z","shell.execute_reply":"2025-07-29T14:47:23.097923Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"trace1 = go.Bar(\n    y = tmp[tmp['Gesture_Ratio'] > 85]['sequence_id'],\n    x = tmp[tmp['Gesture_Ratio'] > 85]['Gesture_Ratio'],\n    orientation = 'h',\n    name = 'Gesture',\n    marker = dict(color='rgba(255,0,0,0.5)',\n                 line=dict(color='red', width=1)),\n)\ntrace2 = go.Bar(\n    y = tmp[tmp['Gesture_Ratio'] > 85]['sequence_id'],\n    x = tmp[tmp['Gesture_Ratio'] > 85]['Transition_Ratio'],\n    orientation = 'h',\n    name = 'Transition',\n    marker = dict(color='rgba(0,0,255,0.5)',\n                 line=dict(color='blue', width=1)),\n)\ntrace3 = go.Bar(\n    y = tmp[tmp['Transition_Ratio'] > 95]['sequence_id'],\n    x = tmp[tmp['Transition_Ratio'] > 95]['Gesture_Ratio'],\n    orientation = 'h',\n    name = 'Gesture',\n    marker = dict(color='rgba(255,0,0,0.5)',\n                 line=dict(color='red', width=1)),\n    showlegend=False,\n)\ntrace4 = go.Bar(\n    y = tmp[tmp['Transition_Ratio'] > 95]['sequence_id'],\n    x = tmp[tmp['Transition_Ratio'] > 95]['Transition_Ratio'],\n    orientation = 'h',\n    name = 'Transition',\n    marker = dict(color='rgba(0,0,255,0.5)',\n                 line=dict(color='blue', width=1)),\n    showlegend=False,\n)\n\nfig = make_subplots(rows=1, cols=2, subplot_titles=(\"Too many Gesture\", \"Too many Transition\"))\n\nfig.add_traces([trace1,trace2], 1, 1)\nfig.add_traces([trace3,trace4], 1, 2)\n\nfig.update_layout(\n    barmode='stack'\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:23.099107Z","iopub.execute_input":"2025-07-29T14:47:23.099307Z","iopub.status.idle":"2025-07-29T14:47:23.142166Z","shell.execute_reply.started":"2025-07-29T14:47:23.099291Z","shell.execute_reply":"2025-07-29T14:47:23.141612Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gesture_ids = tmp[tmp['Gesture_Ratio'] > 85]['sequence_id'].values\ntransition_ids = tmp[tmp['Transition_Ratio'] > 95]['sequence_id'].values\n\nprint(f\"\\n{Fore.BLUE}{st_} {gesture_ids}\")\nprint(f\"\\n{Fore.BLUE}{st_} {transition_ids}\")\n\nremove_ids = np.unique(np.concatenate([gesture_ids, transition_ids]))\n\ntrain_df = train_df[~train_df['sequence_id'].isin(remove_ids)].reset_index(drop=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:23.142804Z","iopub.execute_input":"2025-07-29T14:47:23.143057Z","iopub.status.idle":"2025-07-29T14:47:25.360983Z","shell.execute_reply.started":"2025-07-29T14:47:23.143039Z","shell.execute_reply":"2025-07-29T14:47:25.359768Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig = go.Figure()\n\ntmp1 = train_df[train_df['sequence_type'] == 'Target']\n\nfor bh in tmp1['phase'].unique():\n    fig.add_trace(\n        go.Box(\n            x=tmp1[tmp1['phase'] == bh]['gesture'],\n            y=tmp1[tmp1['phase'] == bh]['sequence_counter_pct'],\n            name=bh,\n            boxmean='sd'\n        )\n    )\n\n\nfig.update_layout(\n    title= \"Distribution of Sequence Counter per Phase in Target\",\n    boxmode='group' \n)\n\nfig.show(renderer='iframe')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:25.362026Z","iopub.execute_input":"2025-07-29T14:47:25.362324Z","iopub.status.idle":"2025-07-29T14:47:27.199112Z","shell.execute_reply.started":"2025-07-29T14:47:25.362299Z","shell.execute_reply":"2025-07-29T14:47:27.198517Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig = go.Figure()\n\ntmp2 = train_df[train_df['sequence_type'] == 'Non-Target']\n\nfor bh in tmp2['phase'].unique():\n    fig.add_trace(\n        go.Box(\n            x=tmp2[tmp2['phase'] == bh]['gesture'],\n            y=tmp2[tmp2['phase'] == bh]['sequence_counter_pct'],\n            name=bh,\n            boxmean='sd'\n        )\n    )\n\n\nfig.update_layout(\n    title= \"Distribution of Sequence Counter per Phase in Non-Target\",\n    boxmode='group' \n)\n\nfig.show(renderer='iframe')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:27.199744Z","iopub.execute_input":"2025-07-29T14:47:27.200005Z","iopub.status.idle":"2025-07-29T14:47:28.428649Z","shell.execute_reply.started":"2025-07-29T14:47:27.199986Z","shell.execute_reply":"2025-07-29T14:47:28.427912Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gc.collect()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:28.429343Z","iopub.execute_input":"2025-07-29T14:47:28.429609Z","iopub.status.idle":"2025-07-29T14:47:28.738925Z","shell.execute_reply.started":"2025-07-29T14:47:28.429590Z","shell.execute_reply":"2025-07-29T14:47:28.738323Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"##  4. Helios Device: IMU(BN0080/BNO085)\n___","metadata":{},"attachments":{"c65a1614-2d4b-4dcc-8da7-646f43e1fc4d.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### IMU: BN0080/BNO085\n\nAn integrated Sensor that combines an accelerometer, gyroscope and magnetometer to measure linear acceleration, angular velocity, and heading direction\n\n☑️ **Accurate Heading Angle**: Less than 0.5°/min typicla error rate.\n\n☑️ **Tilt Independent Heading:** Allow for proper heading output when surface is uneven\n\n☑️ **Bump Detection**: Calibrated accelerometer output provides data to support a bump detection algorithm without having uses a separate sensor\n\n☑️ **Inclination Dection**: Provieds full 3DOF robot orientation.\n\n\n| **Sensor Type**             | **axis**                                                                                                             |\n| ---------------------- | --------------------------------------------------------------------------------------------------------------------------- |\n| `accelerometer`              | acc_x, acc_y, acc_z                                                                                                     |\n| `gyroscope & magnetometer`          | rot_w, rot_x, rot_y, rot_z |\n\n","metadata":{}},{"cell_type":"code","source":"acc_list = ['acc_x','acc_y','acc_z']\nrot_list = ['rot_x', 'rot_y', 'rot_z','rot_w']\n\nacc_color = ['red','yellow','orange']\nrot_color = ['red', 'yellow', 'yellowgreen', 'skyblue']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:28.739749Z","iopub.execute_input":"2025-07-29T14:47:28.740029Z","iopub.status.idle":"2025-07-29T14:47:28.750176Z","shell.execute_reply.started":"2025-07-29T14:47:28.740007Z","shell.execute_reply":"2025-07-29T14:47:28.749449Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df[acc_list + rot_list].info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:28.751007Z","iopub.execute_input":"2025-07-29T14:47:28.751325Z","iopub.status.idle":"2025-07-29T14:47:28.785040Z","shell.execute_reply.started":"2025-07-29T14:47:28.751302Z","shell.execute_reply":"2025-07-29T14:47:28.784518Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"tmp = train_df.groupby(['sequence_id']).agg(\n    rot_x_na_ratio = ('rot_x', lambda x: x.isna().mean() * 100)\n).reset_index()\n\ntmp['rot_x_na_ratio'].value_counts()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:28.785693Z","iopub.execute_input":"2025-07-29T14:47:28.785884Z","iopub.status.idle":"2025-07-29T14:47:29.398924Z","shell.execute_reply.started":"2025-07-29T14:47:28.785868Z","shell.execute_reply":"2025-07-29T14:47:29.398348Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"rot_nan_list = tmp[tmp['rot_x_na_ratio'] == 100]['sequence_id'].values\nrot_nan_list","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:29.399500Z","iopub.execute_input":"2025-07-29T14:47:29.399794Z","iopub.status.idle":"2025-07-29T14:47:29.408542Z","shell.execute_reply.started":"2025-07-29T14:47:29.399769Z","shell.execute_reply":"2025-07-29T14:47:29.407709Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Rapid Umap Visualization","metadata":{}},{"cell_type":"code","source":"import cudf, cuml, cupy\nprint('RAPIDS', cudf.__version__)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:29.409520Z","iopub.execute_input":"2025-07-29T14:47:29.410047Z","iopub.status.idle":"2025-07-29T14:47:29.421135Z","shell.execute_reply.started":"2025-07-29T14:47:29.410022Z","shell.execute_reply":"2025-07-29T14:47:29.420334Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from cuml import UMAP\numap = UMAP()\n\ncols = acc_list + rot_list\nstats = ['mean', 'min', 'max', 'std'] \n\nagg_dict = {col: stats for col in cols}\ntmp = cudf.from_pandas(train_df.dropna().reset_index(drop=True))\n\ntmp_stats = tmp.groupby('sequence_id').agg(agg_dict)\n\ntmp_stats.columns = [f\"{col}_{stat}\" for col, stat in tmp_stats.columns]\n\ntmp_stats = tmp_stats.reset_index()\n\ntmp_meta = tmp[['sequence_id', 'gesture', 'sequence_type']]\\\n    .drop_duplicates(subset=['sequence_id'])\\\n    .dropna()\n\ntmp = tmp_stats.merge(tmp_meta, on='sequence_id', how='left')\n\ntmp = tmp.to_pandas()\ntmp.loc[tmp['sequence_type'] != 'Target', 'gesture'] = 'Non-Target'\ntmp_cudf = cudf.from_pandas(tmp)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:29.421996Z","iopub.execute_input":"2025-07-29T14:47:29.422254Z","iopub.status.idle":"2025-07-29T14:47:32.664277Z","shell.execute_reply.started":"2025-07-29T14:47:29.422234Z","shell.execute_reply":"2025-07-29T14:47:32.663458Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"%%time\n\nfeatures = tmp_cudf.drop(columns=['sequence_id','gesture','sequence_type'])\n\nembed_2d = umap.fit_transform(features)\nembed_2d = embed_2d.to_numpy()\nembed_df = pd.DataFrame(embed_2d, columns=['x','y'])\nembed_df['gesture'] = tmp['gesture'].values\n\nfig = go.Figure()\n\nfor ges in tmp['gesture'].unique():\n    tmp2 = embed_df[embed_df['gesture'] == ges]\n    \n    fig.add_trace(\n        go.Scatter(\n            x = tmp2['x'],\n            y = tmp2['y'],\n            mode = 'markers',\n            marker=dict(size=6, opacity=0.7),\n            name = ges,\n        )\n    )\n\nfig.update_layout(\n    title='Correlation with Gesture & Thermopile Sensor',\n    legend=dict(title='Gesture Type', bordercolor='black', borderwidth=1)\n    \n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:32.665361Z","iopub.execute_input":"2025-07-29T14:47:32.665624Z","iopub.status.idle":"2025-07-29T14:47:32.801648Z","shell.execute_reply.started":"2025-07-29T14:47:32.665599Z","shell.execute_reply":"2025-07-29T14:47:32.800937Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Acceleration | IMU Device","metadata":{}},{"cell_type":"code","source":"fig = make_subplots(rows=1, cols=2, subplot_titles=(\"Transition\",\"Gesture\"))\n\ntmp1 = train_df[train_df['phase'] == 'Transition']\n\nfor acc, col in zip(acc_list, acc_color):\n    fig.add_trace(\n        go.Box(\n            x=tmp1['sequence_type'],\n            y=tmp1[acc],\n            name=acc,\n            marker_color=col,\n            boxmean='sd'\n        ), 1, 1\n        )\n            \ntmp2 = train_df[train_df['phase'] == 'Gesture']\n\nfor acc, col in zip(acc_list, acc_color):\n    fig.add_trace(\n        go.Box(\n            x=tmp2['sequence_type'],\n            y=tmp2[acc],\n            name=acc,\n            marker_color=col,\n            boxmean='sd',\n            showlegend=False\n        ), 1, 2\n        )\n\nfig.update_layout(\n    title= \"Distribution of Accelerator\",\n    boxmode='group' \n)\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:32.802394Z","iopub.execute_input":"2025-07-29T14:47:32.802658Z","iopub.status.idle":"2025-07-29T14:47:35.624326Z","shell.execute_reply.started":"2025-07-29T14:47:32.802641Z","shell.execute_reply":"2025-07-29T14:47:35.623576Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================================\n# Outlier Removal for Specific Gesture\n# ============================================================================\n# In general, when 'phase' is 'Gesture', no gesture shows acc_z values over 20.\n# However, the gesture 'Forehead - pull hairline' includes an outlier (acc_z = 23)\n# specifically in sequence_id = 'SEQ_012112'. This outlier will be excluded\n# to maintain consistency in gesture dynamics.\n# =============================================\n\nacc_z_outline = train_df.loc[\n    (train_df['phase'] == 'Gesture') & \n    (train_df['acc_z'] > 20)\n]['sequence_id'].values[0]\n\ntrain_df = train_df[train_df['sequence_id'] != acc_z_outline].reset_index(drop=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:35.625232Z","iopub.execute_input":"2025-07-29T14:47:35.625794Z","iopub.status.idle":"2025-07-29T14:47:36.864242Z","shell.execute_reply.started":"2025-07-29T14:47:35.625765Z","shell.execute_reply":"2025-07-29T14:47:36.863650Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"seq_list = np.random.choice(train_df[train_df['behavior_flow'] == FLOW_1]['sequence_id'].unique(), 3)\n\ntitles = [seq for seq in seq_list]\n\nfig = make_subplots(rows=3, cols=1, subplot_titles=titles)\n\nfor idx, seq_id in enumerate(seq_list, start=1):\n    \n    tmp = train_df[train_df['sequence_id'] == seq_id].reset_index()\n    \n    for acc, col in zip(acc_list, acc_color):\n        fig.add_trace(\n            go.Scatter(\n                x = tmp.index,\n                y = tmp[acc],\n                mode = 'lines',\n                name = acc,\n                marker_color = col,\n                showlegend = (idx == 1),\n            ), idx, 1\n        )\n        \n    fig.add_vrect(\n        x0=0, x1=tmp.iloc[0]['Relaxes and moves hand to target location']-1,\n        fillcolor=\"red\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Transition\",\n        annotation_position=\"top left\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Relaxes and moves hand to target location']-1, x1=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1,\n        fillcolor=\"green\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Pause\",\n        annotation_position=\"top right\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1, x1=len(tmp)-1,\n        fillcolor=\"blue\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Gesture\",\n        annotation_position=\"top right\"\n    )\nfig.update_layout(\n    title=FLOW_1,\n)\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:36.865063Z","iopub.execute_input":"2025-07-29T14:47:36.865266Z","iopub.status.idle":"2025-07-29T14:47:37.500266Z","shell.execute_reply.started":"2025-07-29T14:47:36.865250Z","shell.execute_reply":"2025-07-29T14:47:37.499606Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"seq_list = np.random.choice(train_df[train_df['behavior_flow'] == FLOW_2]['sequence_id'].unique(), 3)\n\ntitles = [seq for seq in seq_list]\n\nfig = make_subplots(rows=3, cols=1, subplot_titles=titles)\n\nfor idx, seq_id in enumerate(seq_list, start=1):\n    \n    tmp = train_df[train_df['sequence_id'] == seq_id].reset_index()\n    \n    for acc, col in zip(acc_list, acc_color):\n        fig.add_trace(\n            go.Scatter(\n                x = tmp.index,\n                y = tmp[acc],\n                mode = 'lines',\n                name = acc,\n                marker_color = col,\n                showlegend = (idx == 1),\n            ), idx, 1\n        )\n        \n    fig.add_vrect(\n        x0=0, x1=tmp.iloc[0]['Moves hand to target location']-1,\n        fillcolor=\"red\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Transition\",\n        annotation_position=\"top left\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Moves hand to target location']-1, x1=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Moves hand to target location'] -1,\n        fillcolor=\"green\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Pause\",\n        annotation_position=\"top right\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Moves hand to target location'] -1, x1=len(tmp)-1,\n        fillcolor=\"blue\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Gesture\",\n        annotation_position=\"top right\"\n    )\nfig.update_layout(\n    title=FLOW_2,\n)\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:37.501109Z","iopub.execute_input":"2025-07-29T14:47:37.501335Z","iopub.status.idle":"2025-07-29T14:47:38.255926Z","shell.execute_reply.started":"2025-07-29T14:47:37.501317Z","shell.execute_reply":"2025-07-29T14:47:38.255220Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Rotation | IMU Device","metadata":{}},{"cell_type":"code","source":"fig = make_subplots(rows=1, cols=2, subplot_titles=(\"Transition\",\"Gesture\"))\n\ntmp1 = train_df[train_df['phase'] == 'Transition']\n\nfor rot, col in zip(rot_list, rot_color):\n    fig.add_trace(\n        go.Box(\n            x=tmp1['sequence_type'],\n            y=tmp1[rot],\n            name=rot,\n            marker_color=col,\n            boxmean='sd'\n        ), 1, 1\n        )\n            \ntmp2 = train_df[train_df['phase'] == 'Gesture']\n\nfor rot, col in zip(rot_list, rot_color):\n    fig.add_trace(\n        go.Box(\n            x=tmp2['sequence_type'],\n            y=tmp2[rot],\n            name=rot,\n            marker_color=col,\n            boxmean='sd',\n            showlegend=False\n        ), 1, 2\n        )\n\nfig.update_layout(\n    title= \"Distribution of Rotation\",\n    boxmode='group' \n)\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:38.256751Z","iopub.execute_input":"2025-07-29T14:47:38.257333Z","iopub.status.idle":"2025-07-29T14:47:41.738124Z","shell.execute_reply.started":"2025-07-29T14:47:38.257315Z","shell.execute_reply":"2025-07-29T14:47:41.737301Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"seq_list = np.random.choice(train_df[train_df['behavior_flow'] == FLOW_1]['sequence_id'].unique(), 3)\n\ntitles = [seq for seq in seq_list]\n\nfig = make_subplots(rows=3, cols=1, subplot_titles=titles)\n\nfor idx, seq_id in enumerate(seq_list, start=1):\n    \n    tmp = train_df[train_df['sequence_id'] == seq_id].reset_index()\n    \n    for rot, col in zip(rot_list, rot_color):\n        fig.add_trace(\n            go.Scatter(\n                x = tmp.index,\n                y = tmp[rot],\n                mode = 'lines',\n                name = rot,\n                marker_color = col,\n                showlegend = (idx == 1),\n            ), idx, 1\n        )\n        \n    fig.add_vrect(\n        x0=0, x1=tmp.iloc[0]['Relaxes and moves hand to target location']-1,\n        fillcolor=\"red\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Transition\",\n        annotation_position=\"top left\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Relaxes and moves hand to target location']-1, x1=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1,\n        fillcolor=\"green\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Pause\",\n        annotation_position=\"top right\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1, x1=len(tmp)-1,\n        fillcolor=\"blue\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Gesture\",\n        annotation_position=\"top right\"\n    )\nfig.update_layout(\n    title=FLOW_1,\n)\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:41.738965Z","iopub.execute_input":"2025-07-29T14:47:41.739136Z","iopub.status.idle":"2025-07-29T14:47:42.364441Z","shell.execute_reply.started":"2025-07-29T14:47:41.739122Z","shell.execute_reply":"2025-07-29T14:47:42.363811Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"seq_list = np.random.choice(train_df[train_df['behavior_flow'] == FLOW_2]['sequence_id'].unique(), 3)\n\ntitles = [seq for seq in seq_list]\n\nfig = make_subplots(rows=3, cols=1, subplot_titles=titles)\n\nfor idx, seq_id in enumerate(seq_list, start=1):\n    \n    tmp = train_df[train_df['sequence_id'] == seq_id].reset_index()\n    \n    for rot, col in zip(rot_list, rot_color):\n        fig.add_trace(\n            go.Scatter(\n                x = tmp.index,\n                y = tmp[rot],\n                mode = 'lines',\n                name = rot,\n                marker_color = col,\n                showlegend = (idx == 1),\n            ), idx, 1\n        )\n        \n    fig.add_vrect(\n        x0=0, x1=tmp.iloc[0]['Moves hand to target location']-1,\n        fillcolor=\"red\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Transition\",\n        annotation_position=\"top left\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Moves hand to target location']-1, x1=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Moves hand to target location'] -1,\n        fillcolor=\"green\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Pause\",\n        annotation_position=\"top right\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Moves hand to target location'] -1, x1=len(tmp)-1,\n        fillcolor=\"blue\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Gesture\",\n        annotation_position=\"top right\"\n    )\nfig.update_layout(\n    title=FLOW_2,\n)\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:42.365189Z","iopub.execute_input":"2025-07-29T14:47:42.365400Z","iopub.status.idle":"2025-07-29T14:47:43.115413Z","shell.execute_reply.started":"2025-07-29T14:47:42.365383Z","shell.execute_reply":"2025-07-29T14:47:43.114844Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ IMU Spectrogram\n\n    mel_spec = librsoa.feature.melspectrogram(y=x, sr=200, hop_length=len(x)//256, n_fft=1024, n_mels=128, fmin=0, fmax=20, win_length=128\n\n☑️ **`y`** is the input time series signal\n\n☑️ **`sr`** is the sampling frequency.\n\n☑️ **`hop_length`** produces image with width=len(x)/hop_length\n\n☑️ **`n_fft`** controls vertical resolution and quality of spectrogram\n\n☑️ **`n_mels`** produces image with height=n_mels\n\n☑️ **`f_min`** is smallest frequency in our spectrogram\n\n☑️ **`f_max`** is largest frequency in our spectrogram\n\n☑️ **`win_length`** controls hortizonal resolution and quality of spectrogram","metadata":{}},{"cell_type":"code","source":"import librosa\nfrom scipy import interpolate\n\ndef inter_signal(x):\n    original_len = len(x)\n    interp_func = interpolate.interp1d(np.linspace(0, 1, original_len), x, kind='linear')\n    x_interp = interp_func(np.linspace(0, 1, 1024))\n    return x_interp\n\ndef spectrogram_from_imu(df, seq_id, sr=64):\n    tmp = df[df['sequence_id'] == seq_id].reset_index()\n\n    img_acc = np.zeros((192,128,3), dtype='float32')\n    img_rot = np.zeros((192,96,4), dtype='float32')\n\n    for i, acc in enumerate(acc_list):\n        x = tmp[acc].values\n        m = np.nanmean(x)\n        x = np.nan_to_num(x, nan=m)\n        x = inter_signal(x)\n\n        mel_spec = librosa.feature.melspectrogram(y=x, sr=sr, hop_length=len(x)//128, n_fft=128,\n                                                  n_mels=192, fmin=0, fmax=sr//2)\n        width = (mel_spec.shape[1]//16)*16\n        mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max).astype(np.float32)[:,:width]\n\n        mel_spec_db = (mel_spec_db+40)/40\n        img_acc[:,:,i] = mel_spec_db\n\n    for i, rot in enumerate(rot_list):\n        x = tmp[rot].values\n        m = np.nanmean(x)\n        x = np.nan_to_num(x, nan=m)\n        x = inter_signal(x)\n\n        mel_spec = librosa.feature.melspectrogram(y=x, sr=sr, hop_length=len(x)//96, n_fft=96,\n                                                  n_mels=192, fmin=0, fmax=sr//2)\n        width = (mel_spec.shape[1]//16)*16\n        mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max).astype(np.float32)[:,:width]\n\n        mel_spec_db = (mel_spec_db+40)/40\n        img_rot[...,i] = mel_spec_db\n\n    \n\n    return img_acc, img_rot","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:43.116122Z","iopub.execute_input":"2025-07-29T14:47:43.116307Z","iopub.status.idle":"2025-07-29T14:47:43.124512Z","shell.execute_reply.started":"2025-07-29T14:47:43.116292Z","shell.execute_reply":"2025-07-29T14:47:43.123989Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"seq_list = np.random.choice(train_df['sequence_id'].unique(), 2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:43.125269Z","iopub.execute_input":"2025-07-29T14:47:43.125524Z","iopub.status.idle":"2025-07-29T14:47:43.163510Z","shell.execute_reply.started":"2025-07-29T14:47:43.125503Z","shell.execute_reply":"2025-07-29T14:47:43.162993Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(14, 6))\n\nfor i, seq in enumerate(seq_list):\n    img_acc, img_rot = spectrogram_from_imu(train_df, seq)\n\n    for j, acc in enumerate(acc_list):\n        plt.subplot(2, 7, 7*i + j + 1)\n        plt.imshow(img_acc[...,j], aspect='auto', origin='lower', cmap='jet')\n        plt.title(f'{seq}_{acc}')\n        plt.axis('off')\n\n    for j, rot in enumerate(rot_list):\n        plt.subplot(2, 7, 7*i + j + 4)\n        plt.imshow(img_rot[...,j], aspect='auto', origin='lower', cmap='jet')\n        plt.title(f'{seq}_{rot}')\n        plt.axis('off')\n\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:43.164128Z","iopub.execute_input":"2025-07-29T14:47:43.164311Z","iopub.status.idle":"2025-07-29T14:47:44.842417Z","shell.execute_reply.started":"2025-07-29T14:47:43.164296Z","shell.execute_reply":"2025-07-29T14:47:44.841749Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(14, 6))\n\nfor i, seq in enumerate(seq_list):\n    img_acc, img_rot = spectrogram_from_imu(train_df, seq)\n\n    img_acc = np.concatenate([img_acc[...,0], img_acc[...,1], img_acc[...,2]], axis=1) # (192, 384)\n    img_rot = np.concatenate([img_rot[...,0], img_rot[...,1], img_rot[...,2], img_rot[...,3]], axis=1) # (192,384)\n\n    img = np.concatenate([img_acc, img_rot], axis=0)\n\n    plt.subplot(1,2,i+1)\n    plt.imshow(img, aspect='auto', origin='lower', cmap='jet')\n\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:44.843313Z","iopub.execute_input":"2025-07-29T14:47:44.843586Z","iopub.status.idle":"2025-07-29T14:47:45.662498Z","shell.execute_reply.started":"2025-07-29T14:47:44.843563Z","shell.execute_reply":"2025-07-29T14:47:45.661779Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gc.collect()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:45.663425Z","iopub.execute_input":"2025-07-29T14:47:45.663755Z","iopub.status.idle":"2025-07-29T14:47:45.921796Z","shell.execute_reply.started":"2025-07-29T14:47:45.663730Z","shell.execute_reply":"2025-07-29T14:47:45.921029Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 5. Helios Device: Thermopile Sensor: MLX90632\n___\n\n![image.png](attachment:567a6c17-6d94-42fe-8aac-c5f50df45ebf.png)\n\n☑️ **Small Size of 3x3mm**\n\n☑️ **Standard measurement resolution: 0.02°C**\n\n☑️ **Medical measurement resolution: 0.01°C**\n\n☑️ **Default refresh rate 0.5s, configurable between 16ms and 2s**\n\nThe sensor measures infrared radition from the target, but extreme environmental conditions can cause measurement erros.\n\nExamples of problematic conditions include:\n\n☑️ Hot electronics located directly behind the sensor\n\n☑️ Heater and coolers positions behind or beside the sensor\n\n☑️ Very hot or cold objects placed very close to the sensor, heating or cooling not just the sensing element but also the sensor package itself.\n\n*The MLX90632 includes internal compensation circuits designed to minimize the effects of these influences and maintain accuracy.*\n\n*In addition, Localized thermal variations in the air, such as turbulence, do not generate thermal noise in the thermopile’s output signal.*\n\n**Due to the physical properties of infrared radiation, it is normal that the noise decreases as the target temperature increases, and the noise increases as the target temperature decreases**","metadata":{},"attachments":{"567a6c17-6d94-42fe-8aac-c5f50df45ebf.png":{"image/png":"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"}}},{"cell_type":"code","source":"thm_list = ['thm_1','thm_2','thm_3','thm_4','thm_5']\nthm_color = ['#FF5733',  '#33C1FF',  '#33FF57', '#FFC300','#8E44AD'] ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:45.922719Z","iopub.execute_input":"2025-07-29T14:47:45.922993Z","iopub.status.idle":"2025-07-29T14:47:45.932688Z","shell.execute_reply.started":"2025-07-29T14:47:45.922975Z","shell.execute_reply":"2025-07-29T14:47:45.931954Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df[thm_list].info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:45.933406Z","iopub.execute_input":"2025-07-29T14:47:45.933681Z","iopub.status.idle":"2025-07-29T14:47:45.960388Z","shell.execute_reply.started":"2025-07-29T14:47:45.933661Z","shell.execute_reply":"2025-07-29T14:47:45.959882Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"tmp = train_df.groupby(['sequence_id']).agg(\n    thm_1_na_ratio = ('thm_1', lambda x: x.isna().mean()*100),\n    thm_2_na_ratio = ('thm_2', lambda x: x.isna().mean()*100),\n    thm_3_na_ratio = ('thm_3', lambda x: x.isna().mean()*100),\n    thm_4_na_ratio = ('thm_4', lambda x: x.isna().mean()*100),\n    thm_5_na_ratio = ('thm_5', lambda x: x.isna().mean()*100),\n\n).reset_index()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:45.960969Z","iopub.execute_input":"2025-07-29T14:47:45.961147Z","iopub.status.idle":"2025-07-29T14:47:48.781916Z","shell.execute_reply.started":"2025-07-29T14:47:45.961132Z","shell.execute_reply":"2025-07-29T14:47:48.781327Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"thm_1_na_list = tmp[tmp['thm_1_na_ratio'] > 50]['sequence_id'].values\nthm_2_na_list = tmp[tmp['thm_2_na_ratio'] > 50]['sequence_id'].values\nthm_3_na_list = tmp[tmp['thm_3_na_ratio'] > 50]['sequence_id'].values\nthm_4_na_list = tmp[tmp['thm_4_na_ratio'] > 50]['sequence_id'].values\nthm_5_na_list = tmp[tmp['thm_5_na_ratio'] > 50]['sequence_id'].values\n\nall_ids = np.concatenate([thm_1_na_list, thm_2_na_list, thm_3_na_list, thm_4_na_list, thm_5_na_list])\nthm_na_list = np.unique(all_ids)\nlen(thm_na_list)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:48.782711Z","iopub.execute_input":"2025-07-29T14:47:48.782946Z","iopub.status.idle":"2025-07-29T14:47:48.793243Z","shell.execute_reply.started":"2025-07-29T14:47:48.782926Z","shell.execute_reply":"2025-07-29T14:47:48.792503Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Rapid Umap Visualization","metadata":{}},{"cell_type":"code","source":"cols = thm_list\nstats = ['mean', 'min', 'max', 'std'] \n\nagg_dict = {col: stats for col in cols}\ntmp = cudf.from_pandas(train_df.dropna().reset_index(drop=True))\n\ntmp_stats = tmp.groupby('sequence_id').agg(agg_dict)\n\ntmp_stats.columns = [f\"{col}_{stat}\" for col, stat in tmp_stats.columns]\n\ntmp_stats = tmp_stats.reset_index()\n\ntmp_meta = tmp[['sequence_id', 'gesture', 'sequence_type']]\\\n    .drop_duplicates(subset=['sequence_id'])\\\n    .dropna()\n\ntmp = tmp_stats.merge(tmp_meta, on='sequence_id', how='left')\n\ntmp = tmp.to_pandas()\ntmp.loc[tmp['sequence_type'] != 'Target', 'gesture'] = 'Non-Target'\ntmp_cudf = cudf.from_pandas(tmp)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:48.794106Z","iopub.execute_input":"2025-07-29T14:47:48.794356Z","iopub.status.idle":"2025-07-29T14:47:51.966579Z","shell.execute_reply.started":"2025-07-29T14:47:48.794331Z","shell.execute_reply":"2025-07-29T14:47:51.965991Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"%%time\n\nfeatures = tmp_cudf.drop(columns=['sequence_id','gesture','sequence_type'])\n\nembed_2d = umap.fit_transform(features)\nembed_2d = embed_2d.to_numpy()\nembed_df = pd.DataFrame(embed_2d, columns=['x','y'])\nembed_df['gesture'] = tmp['gesture'].values\n\nfig = go.Figure()\n\nfor ges in tmp['gesture'].unique():\n    tmp2 = embed_df[embed_df['gesture'] == ges]\n    \n    fig.add_trace(\n        go.Scatter(\n            x = tmp2['x'],\n            y = tmp2['y'],\n            mode = 'markers',\n            marker=dict(size=6, opacity=0.7),\n            name = ges,\n        )\n    )\n\nfig.update_layout(\n    title='Correlation with Gesture & IMU Devices',\n    legend=dict(title='Gesture Type', bordercolor='black', borderwidth=1)\n    \n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:51.967350Z","iopub.execute_input":"2025-07-29T14:47:51.967580Z","iopub.status.idle":"2025-07-29T14:47:52.098922Z","shell.execute_reply.started":"2025-07-29T14:47:51.967563Z","shell.execute_reply":"2025-07-29T14:47:52.098221Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Correlation with Thermopile Devices","metadata":{}},{"cell_type":"code","source":"tmp = tmp.reset_index()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:52.099856Z","iopub.execute_input":"2025-07-29T14:47:52.100072Z","iopub.status.idle":"2025-07-29T14:47:52.104982Z","shell.execute_reply.started":"2025-07-29T14:47:52.100055Z","shell.execute_reply":"2025-07-29T14:47:52.104014Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"\\n{Fore.BLUE}{st_} ☑️ All thermopile devices show a strong correlation (above 0.7) with each other, indicating closely related behavior. \\n ☑️ However, Thermopile 3 stands out as it has relatively low correlation with all other devices, suggesting that it should be examined more carefully\")\n\n\nimport plotly.express as px\n\nfeatures =  ['thm_1_mean','thm_2_mean','thm_3_mean','thm_4_mean','thm_5_mean']\n\ncorr_matrix = tmp[features].corr()\n\nfig = px.imshow(corr_matrix,\n                text_auto=True,\n                color_continuous_scale='RdBu_r',\n                zmin=-1, zmax=1,\n                aspect='auto',\n                title='Correlation Matrix')\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:52.105812Z","iopub.execute_input":"2025-07-29T14:47:52.106004Z","iopub.status.idle":"2025-07-29T14:47:52.162231Z","shell.execute_reply.started":"2025-07-29T14:47:52.105989Z","shell.execute_reply":"2025-07-29T14:47:52.161716Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Thermopile Animation","metadata":{}},{"cell_type":"code","source":"def draw_bbox(image, box, label):\n    output = image.copy()\n\n    text_width, text_height = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.4, 1)[0]\n\n    cv2.rectangle(output, (box[0], box[1]-text_height-5),\n                  (box[0]+text_width-5, box[1]), (255,255,255), -1)\n    cv2.putText(output, label, (box[0], box[1]-3),\n                cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0,0,200), 1, cv2.LINE_AA)\n    \n    return output","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:52.162912Z","iopub.execute_input":"2025-07-29T14:47:52.163185Z","iopub.status.idle":"2025-07-29T14:47:52.168030Z","shell.execute_reply.started":"2025-07-29T14:47:52.163161Z","shell.execute_reply":"2025-07-29T14:47:52.167446Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from matplotlib import animation, rc, cm\nfrom matplotlib.colors import Normalize\nimport cv2\n\nrc('animation', html='jshtml')\n\ndef get_colormap_color(value, vmin, vmax, cmap_name='coolwarm'):\n    norm = Normalize(vmin=vmin, vmax=vmax)\n    cmap = cm.get_cmap(cmap_name)\n    rgba = cmap(norm(value))\n    rgb = tuple(int(255 * x) for x in rgba[:3])\n    return rgb  # (R, G, B)\n\ndef create_animation(sequence_id):\n    ims = []\n\n    tmp = train_df[train_df['sequence_id'] == sequence_id].reset_index()\n\n    all_thm_values = tmp[[f'thm_{i}' for i in range(1, 6)]].values.flatten()\n    vmin = np.nanmin(all_thm_values)\n    vmax = np.nanmax(all_thm_values)\n\n\n    for i, row in tqdm(tmp.iterrows(), desc='Create Thermopile Image'):\n\n        img = np.zeros((256,256,3), dtype='float32')\n        \n        color1 = get_colormap_color(row['thm_1'], vmin, vmax)\n        color2 = get_colormap_color(row['thm_2'], vmin, vmax)\n        color3 = get_colormap_color(row['thm_3'], vmin, vmax)\n        color4 = get_colormap_color(row['thm_4'], vmin, vmax)\n        color5 = get_colormap_color(row['thm_5'], vmin, vmax)\n\n        # Thm_1\n        cv2.rectangle(img, (150, 70), (170, 90), color=color1, thickness=-1)\n        cv2.rectangle(img, (150, 70), (170, 90), color=(0, 0, 255), thickness=1)\n        img = draw_bbox(img, (150, 70, 170, 90), label=f'1: {row[\"thm_1\"]:.1f}')\n        # Thm_2\n        cv2.rectangle(img, (150, 30), (170, 50), color=color2, thickness=-1)\n        cv2.rectangle(img, (150, 30), (170, 50), color=(0, 0, 255), thickness=1)\n        img = draw_bbox(img, (150, 30, 170, 50), label=f'2: {row[\"thm_2\"]:.1f}')\n        # Thm_3\n        cv2.rectangle(img, (190, 100), (210, 120), color=color3, thickness=-1)\n        cv2.rectangle(img, (190, 100), (210, 120), color=(0, 0, 255), thickness=1)\n        img = draw_bbox(img, (190, 100, 210, 120), label=f'3: {row[\"thm_3\"]:.1f}')\n        # Thm_4\n        cv2.rectangle(img, (150, 210), (170, 230), color=color4, thickness=-1)\n        cv2.rectangle(img, (150, 210), (170, 230), color=(0, 0, 255), thickness=1)\n        img = draw_bbox(img, (150, 210, 170, 230), label=f'4: {row[\"thm_4\"]:.1f}')\n        # Thm_5\n        cv2.rectangle(img, (30, 100), (50, 120), color=color5, thickness=-1)\n        cv2.rectangle(img, (30, 100), (50, 120), color=(0, 0, 255), thickness=1)\n        img = draw_bbox(img, (30, 100, 50, 120), label=f'5: {row[\"thm_5\"]:.1f}')\n\n        ims.append(img)\n\n    fig = plt.figure(figsize=(6,6))\n    plt.axis('off')\n    im = plt.imshow(ims[0].astype(np.uint8))\n\n    def animate_func(i):\n        im.set_array(ims[i].astype(np.uint8))\n        return [im]\n        \n    plt.close(fig)\n\n    return animation.FuncAnimation(fig, animate_func, frames=len(ims), interval = 1000 // 24)\n\nseq_id = np.random.choice(train_df['sequence_id'].unique())\nanim = create_animation(seq_id)\nanim","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:52.168852Z","iopub.execute_input":"2025-07-29T14:47:52.169069Z","iopub.status.idle":"2025-07-29T14:47:56.345563Z","shell.execute_reply.started":"2025-07-29T14:47:52.169046Z","shell.execute_reply":"2025-07-29T14:47:56.344882Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"\\n{Fore.BLUE}{st_} ☑️ Unlike the other thermopiles, thm_3 has values of zero, which are considered outliers. This likely explains why the correlation of thm_3_mean with the means of other thermopiles is low in the heatmap above, as the presence of zeros may have distorted the mean values\")\n\n\ntmp = train_df[~train_df['sequence_id'].isin(thm_na_list)].reset_index(drop=True).copy()\n\nplt.style.use('Solarize_Light2')\n\nfig, axes = plt.subplots(2, 3, figsize=(12, 6))\naxes = axes.flatten()\ncolor_list = ['blue', 'red']\n\nsns.set(style='whitegrid', context='notebook')\nfor i in range(5):\n    sns.boxplot(\n        x='phase', y=f'thm_{i+1}', data=tmp,\n        palette=dict(zip(tmp['phase'].unique(), color_list)),\n        ax=axes[i], showmeans=True\n    )\n    axes[i].set_title(f'thm_{i}')\n\n\naxes[5].axis('off')\n\nplt.suptitle('<Thermopile with Phase>', fontweight='bold')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:56.346433Z","iopub.execute_input":"2025-07-29T14:47:56.346699Z","iopub.status.idle":"2025-07-29T14:47:59.780081Z","shell.execute_reply.started":"2025-07-29T14:47:56.346678Z","shell.execute_reply":"2025-07-29T14:47:59.779325Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"\\n{Fore.BLUE}{st_} ☑️ As is well understood, during the Pause Phase, Thermopile readings tend to remain stable with minimal fluctuations\")\n\nseq_list = np.random.choice(train_df[train_df['behavior_flow'] == FLOW_1]['sequence_id'].unique(), 3)\n\ntitles = [seq for seq in seq_list]\n\nfig = make_subplots(rows=3, cols=1, subplot_titles=titles)\n\nfor idx, seq_id in enumerate(seq_list, start=1):\n    \n    tmp = train_df[train_df['sequence_id'] == seq_id].reset_index()\n    \n    for thm, col in zip(thm_list, thm_color):\n        fig.add_trace(\n            go.Scatter(\n                x = tmp.index,\n                y = tmp[thm],\n                mode = 'lines',\n                name = thm,\n                marker_color = col,\n                showlegend = (idx == 1),\n            ), idx, 1\n        )\n        \n    fig.add_vrect(\n        x0=0, x1=tmp.iloc[0]['Relaxes and moves hand to target location']-1,\n        fillcolor=\"red\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Transition\",\n        annotation_position=\"top left\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Relaxes and moves hand to target location']-1, x1=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1,\n        fillcolor=\"green\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Pause\",\n        annotation_position=\"top right\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1, x1=len(tmp)-1,\n        fillcolor=\"blue\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Gesture\",\n        annotation_position=\"top right\"\n    )\nfig.update_layout(\n    title=FLOW_1,\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:47:59.780828Z","iopub.execute_input":"2025-07-29T14:47:59.781094Z","iopub.status.idle":"2025-07-29T14:48:00.410543Z","shell.execute_reply.started":"2025-07-29T14:47:59.781067Z","shell.execute_reply":"2025-07-29T14:48:00.409923Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Remove Thermopile Outliers","metadata":{}},{"cell_type":"code","source":"tmp = train_df[~train_df['sequence_id'].isin(thm_na_list)].reset_index(drop=True)\nthm_3_0 = tmp[tmp['thm_3'] == 0]['sequence_id'].unique()\ntmp = tmp[~tmp['sequence_id'].isin(thm_3_0)].reset_index(drop=True)\n\ntmp = tmp[tmp['behavior'] == 'Hand at target location'].groupby('sequence_id').agg(\n    pause_diff_1 = ('thm_1', lambda x: np.mean(np.abs(np.diff(x)))),\n    pause_diff_2 = ('thm_2', lambda x: np.mean(np.abs(np.diff(x)))),\n    pause_diff_3 = ('thm_3', lambda x: np.mean(np.abs(np.diff(x)))),\n    pause_diff_4 = ('thm_4', lambda x: np.mean(np.abs(np.diff(x)))),\n    pause_diff_5 = ('thm_5', lambda x: np.mean(np.abs(np.diff(x)))),\n    gesture = ('gesture', lambda x: x.iloc[0]),\n).reset_index()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:00.411206Z","iopub.execute_input":"2025-07-29T14:48:00.411651Z","iopub.status.idle":"2025-07-29T14:48:04.459402Z","shell.execute_reply.started":"2025-07-29T14:48:00.411632Z","shell.execute_reply":"2025-07-29T14:48:04.458644Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig = go.Figure()\n\nfor i, col in enumerate(thm_color, start=1):\n    fig.add_trace(\n        go.Box(\n            y = tmp[f'pause_diff_{i}'],\n            name = f'pause_diff_{i}',\n            marker_color = col,\n            boxmean='sd',)\n    )\n\nfig.add_hrect(\n    y0=3,\n    y1=4,\n    fillcolor=\"red\",\n    opacity=0.2,\n    layer=\"below\",\n    line_width=0\n)\n\nfig.update_layout(\n    title=\"Ditribution of Thermopile Diff in Pause Phase\"\n)\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:04.460154Z","iopub.execute_input":"2025-07-29T14:48:04.460366Z","iopub.status.idle":"2025-07-29T14:48:04.503642Z","shell.execute_reply.started":"2025-07-29T14:48:04.460349Z","shell.execute_reply":"2025-07-29T14:48:04.502912Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"seq_list = ['SEQ_012617']\n\ntitles = [seq for seq in seq_list]\n\nfig = go.Figure()\n    \ntmp = train_df[train_df['sequence_id'] == seq_list[0]].reset_index()\n    \nfor thm, col in zip(thm_list, thm_color):\n        fig.add_trace(\n            go.Scatter(\n                x = tmp.index,\n                y = tmp[thm],\n                mode = 'lines',\n                name = thm,\n                marker_color = col,\n            ), \n        )\n        \nfig.add_vrect(\n        x0=0, x1=tmp.iloc[0]['Relaxes and moves hand to target location']-1,\n        fillcolor=\"red\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Transition\",\n        annotation_position=\"top left\"\n    )\nfig.add_vrect(\n        x0=tmp.iloc[0]['Relaxes and moves hand to target location']-1, x1=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1,\n        fillcolor=\"green\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Pause\",\n        annotation_position=\"top right\"\n    )\nfig.add_vrect(\n        x0=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1, x1=len(tmp)-1,\n        fillcolor=\"blue\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Gesture\",\n        annotation_position=\"top right\"\n    )\nfig.update_layout(\n    title=FLOW_1,\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:04.504539Z","iopub.execute_input":"2025-07-29T14:48:04.504769Z","iopub.status.idle":"2025-07-29T14:48:04.590666Z","shell.execute_reply.started":"2025-07-29T14:48:04.504752Z","shell.execute_reply":"2025-07-29T14:48:04.589994Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"gc.collect()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:04.591582Z","iopub.execute_input":"2025-07-29T14:48:04.591779Z","iopub.status.idle":"2025-07-29T14:48:04.819090Z","shell.execute_reply.started":"2025-07-29T14:48:04.591764Z","shell.execute_reply":"2025-07-29T14:48:04.818419Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 6. Helios Device: Time-of-Flight Sensor: VL53L7CX\n___\n\n![image.png](attachment:38e100ea-ec18-4e12-8087-06977480a62e.png)\n\nA sensor that measures distances by detecting how long it takes for emitted infrared light to bounce back from objects.\n\n☑️ Histogram processing and algorithmic compensation minmizes or removes the impact of cover glass crosstalk.\n\n☑️ **Emitter: 940 nm invisible light vrtical-cavity surface-emitting laser(VCSEL)**\n\n![image.png](attachment:8b5bb962-d2d4-49d7-8a32-b9cdb453afb9.png)\n\nThis data is collected row-wise, where the first pixel could be considered in the top-left of the grid, with the second to its right, ultimately wrapping so the final value is in the bottom right.\n\n✅ **If there is no sensor response(if there is no nearby object causing a signal reflection), -1 is present in this field**. \n\n✅ Units are uncalibrated ***sensor values in the range 0~254.***\n\n✅ Each sensor contains ***64 pixels arranged in an 8x8 grid***, visualized in the 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"},"8b5bb962-d2d4-49d7-8a32-b9cdb453afb9.png":{"image/png":"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"}}},{"cell_type":"code","source":"tof_list = [col for col in train_df.columns if 'tof' in col ]\n\ntof_1_list = tof_list[:64]\ntof_2_list = tof_list[64:128]\ntof_3_list = tof_list[128:192]\ntof_4_list = tof_list[192:256]\ntof_5_list = tof_list[256:320]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:04.819964Z","iopub.execute_input":"2025-07-29T14:48:04.820165Z","iopub.status.idle":"2025-07-29T14:48:04.829278Z","shell.execute_reply.started":"2025-07-29T14:48:04.820145Z","shell.execute_reply":"2025-07-29T14:48:04.828661Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def nan_ratio_mean(df, cols):\n    return df[cols].isna().mean(axis=1).groupby(df['sequence_id']).mean() * 100\n\ntof_1_na = nan_ratio_mean(train_df, tof_1_list).rename('tof_1_na_ratio')\ntof_2_na = nan_ratio_mean(train_df, tof_2_list).rename('tof_2_na_ratio')\ntof_3_na = nan_ratio_mean(train_df, tof_3_list).rename('tof_3_na_ratio')\ntof_4_na = nan_ratio_mean(train_df, tof_4_list).rename('tof_4_na_ratio')\ntof_5_na = nan_ratio_mean(train_df, tof_5_list).rename('tof_5_na_ratio')\n\ntmp = pd.concat([tof_1_na, tof_2_na, tof_3_na, tof_4_na, tof_5_na], axis=1).reset_index()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:04.829996Z","iopub.execute_input":"2025-07-29T14:48:04.830288Z","iopub.status.idle":"2025-07-29T14:48:06.162616Z","shell.execute_reply.started":"2025-07-29T14:48:04.830271Z","shell.execute_reply":"2025-07-29T14:48:06.162056Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"tof_1_na_list = tmp[tmp['tof_1_na_ratio'] > 50]['sequence_id'].values\ntof_2_na_list = tmp[tmp['tof_2_na_ratio'] > 50]['sequence_id'].values\ntof_3_na_list = tmp[tmp['tof_3_na_ratio'] > 50]['sequence_id'].values\ntof_4_na_list = tmp[tmp['tof_4_na_ratio'] > 50]['sequence_id'].values\ntof_5_na_list = tmp[tmp['tof_5_na_ratio'] > 50]['sequence_id'].values\n\nall_ids = np.concatenate([tof_1_na_list, tof_2_na_list, tof_3_na_list, tof_4_na_list, tof_5_na_list])\ntof_na_list = np.unique(all_ids)\nlen(tof_na_list)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:06.163282Z","iopub.execute_input":"2025-07-29T14:48:06.163462Z","iopub.status.idle":"2025-07-29T14:48:06.173360Z","shell.execute_reply.started":"2025-07-29T14:48:06.163449Z","shell.execute_reply":"2025-07-29T14:48:06.172807Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Rapid Umap Visualization","metadata":{}},{"cell_type":"code","source":"from cuml import UMAP\numap = UMAP()\n\ncols = tof_list\nstats = ['mean', 'min', 'max', 'std'] \n\nagg_dict = {col: stats for col in cols}\ntmp = cudf.from_pandas(train_df.dropna().reset_index(drop=True))\n\ntmp_stats = tmp.groupby('sequence_id').agg(agg_dict)\n\ntmp_stats.columns = [f\"{col}_{stat}\" for col, stat in tmp_stats.columns]\n\ntmp_stats = tmp_stats.reset_index()\n\ntmp_meta = tmp[['sequence_id', 'gesture', 'sequence_type']]\\\n    .drop_duplicates(subset=['sequence_id'])\\\n    .dropna()\n\ntmp = tmp_stats.merge(tmp_meta, on='sequence_id', how='left')\n\ntmp = tmp.to_pandas()\ntmp.loc[tmp['sequence_type'] != 'Target', 'gesture'] = 'Non-Target'\ntmp_cudf = cudf.from_pandas(tmp)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:06.174052Z","iopub.execute_input":"2025-07-29T14:48:06.174226Z","iopub.status.idle":"2025-07-29T14:48:11.608688Z","shell.execute_reply.started":"2025-07-29T14:48:06.174213Z","shell.execute_reply":"2025-07-29T14:48:11.607877Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"%%time\n\nfeatures = tmp_cudf.drop(columns=['sequence_id','gesture','sequence_type'])\n\nembed_2d = umap.fit_transform(features)\nembed_2d = embed_2d.to_numpy()\nembed_df = pd.DataFrame(embed_2d, columns=['x','y'])\nembed_df['gesture'] = tmp['gesture'].values\n\nfig = go.Figure()\n\nfor ges in tmp['gesture'].unique():\n    tmp2 = embed_df[embed_df['gesture'] == ges]\n    \n    fig.add_trace(\n        go.Scatter(\n            x = tmp2['x'],\n            y = tmp2['y'],\n            mode = 'markers',\n            marker=dict(size=6, opacity=0.7),\n            name = ges,\n        )\n    )\n\nfig.update_layout(\n    title='Correlation with Gesture & Time of Flight',\n    legend=dict(title='Gesture Type', bordercolor='black', borderwidth=1)\n    \n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:11.609584Z","iopub.execute_input":"2025-07-29T14:48:11.609949Z","iopub.status.idle":"2025-07-29T14:48:12.120553Z","shell.execute_reply.started":"2025-07-29T14:48:11.609924Z","shell.execute_reply":"2025-07-29T14:48:12.119924Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Correlation with TOF Devices","metadata":{}},{"cell_type":"code","source":"print(f\"\\n{Fore.BLUE}{st_} TOF devices that are physically closer to each other tend to have higher correlations. For example, TOF3 and TOF5, as well as TOF2 and TOF4, which are farther apart, show relatively lower correlations.\")\n\ntrain_df['tof_1_mean'] = np.mean(train_df[tof_1_list], axis=1)\ntrain_df['tof_2_mean'] = np.mean(train_df[tof_2_list], axis=1)\ntrain_df['tof_3_mean'] = np.mean(train_df[tof_3_list], axis=1)\ntrain_df['tof_4_mean'] = np.mean(train_df[tof_4_list], axis=1)\ntrain_df['tof_5_mean'] = np.mean(train_df[tof_5_list], axis=1)\n\nfeatures =  ['tof_1_mean','tof_2_mean','tof_3_mean','tof_4_mean','tof_5_mean']\n\ncorr_matrix = train_df[features].corr()\n\nfig = px.imshow(corr_matrix,\n                text_auto=True,\n                color_continuous_scale='RdBu_r',\n                zmin=-1, zmax=1,\n                aspect='auto',\n                title='Correlation Matrix')\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:12.121221Z","iopub.execute_input":"2025-07-29T14:48:12.121464Z","iopub.status.idle":"2025-07-29T14:48:14.503831Z","shell.execute_reply.started":"2025-07-29T14:48:12.121437Z","shell.execute_reply":"2025-07-29T14:48:14.503219Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Time-of-Flight Sensor Animation","metadata":{}},{"cell_type":"code","source":"def create_each_tof(sequence_id, tof_id):\n    ims = []\n\n    tmp = train_df[train_df['sequence_id'] == sequence_id].reset_index()\n\n    for i, row in tqdm(tmp.iterrows(), desc='Create TOF Image'):\n\n        img = np.zeros((128,128), dtype='float32')\n        \n        for i in range(8):\n            for j in range(8):\n                img[i*16:(i+1)*16,j*16:(j+1)*16] = row[f'tof_{tof_id}_v{8*i+j}']\n\n        ims.append(img)\n\n    fig = plt.figure(figsize=(6,6))\n    plt.title(f'TOF Device: {tof_id}\\n Sequence_id: {sequence_id}')\n    plt.axis('off')\n    im = plt.imshow(ims[0].astype(np.uint8), cmap='coolwarm', vmin=np.min(img), vmax=np.max(img))\n\n    def animate_func(i):\n        im.set_array(ims[i].astype(np.uint8))\n        return [im]\n        \n    plt.close(fig)\n\n    return animation.FuncAnimation(fig, animate_func, frames=len(ims), interval = 1000 // 24)\n\nseq_id = np.random.choice(train_df['sequence_id'].unique())\nanim = create_each_tof(seq_id, tof_id=1)\nanim","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:14.504629Z","iopub.execute_input":"2025-07-29T14:48:14.504944Z","iopub.status.idle":"2025-07-29T14:48:20.956134Z","shell.execute_reply.started":"2025-07-29T14:48:14.504919Z","shell.execute_reply":"2025-07-29T14:48:20.955209Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n\ndef get_colormap_color(value, vmin, vmax, cmap_name='coolwarm'):\n    norm = Normalize(vmin=vmin, vmax=vmax)\n    cmap = cm.get_cmap(cmap_name)\n    rgba = cmap(norm(value))\n    rgb = tuple(int(255 * x) for x in rgba[:3])\n    return rgb  # (R, G, B)\n\ndef create_tof(sequence_id):\n    ims = []\n\n    tmp = train_df[train_df['sequence_id'] == sequence_id].reset_index()\n\n    all_thm_values = tmp[[f'thm_{i}' for i in range(1, 6)]].values.flatten()\n    vmin = np.nanmin(all_thm_values)\n    vmax = np.nanmax(all_thm_values)\n\n\n    for i, row in tqdm(tmp.iterrows(), desc='Create Thermopile Image'):\n\n        img = np.zeros((256,256,3), dtype='float32')\n        \n        color1 = get_colormap_color(row['tof_1_mean'], vmin, vmax)\n        color2 = get_colormap_color(row['tof_2_mean'], vmin, vmax)\n        color3 = get_colormap_color(row['tof_3_mean'], vmin, vmax)\n        color4 = get_colormap_color(row['tof_4_mean'], vmin, vmax)\n        color5 = get_colormap_color(row['tof_5_mean'], vmin, vmax)\n\n        # Thm_1\n        cv2.rectangle(img, (130, 70), (150, 90), color=color1, thickness=-1)\n        cv2.rectangle(img, (130, 70), (150, 90), color=(255, 0, 0), thickness=1)\n        img = draw_bbox(img, (130, 70, 150, 90), label=f'1: {row[\"tof_1_mean\"]:.1f}')\n        # Thm_2\n        cv2.rectangle(img, (130, 30), (150, 50), color=color2, thickness=-1)\n        cv2.rectangle(img, (130, 30), (150, 50), color=(255, 0, 0), thickness=1)\n        img = draw_bbox(img, (130, 30, 150, 50), label=f'2: {row[\"tof_2_mean\"]:.1f}')\n        # Thm_3\n        cv2.rectangle(img, (190, 120), (210, 140), color=color3, thickness=-1)\n        cv2.rectangle(img, (190, 120), (210, 140), color=(255, 0, 0), thickness=1)\n        img = draw_bbox(img, (190, 120, 210, 140), label=f'3: {row[\"tof_3_mean\"]:.1f}')\n        # Thm_4\n        cv2.rectangle(img, (130, 210), (150, 230), color=color4, thickness=-1)\n        cv2.rectangle(img, (130, 210), (150, 230), color=(255, 0, 0), thickness=1)\n        img = draw_bbox(img, (130, 210, 150, 230), label=f'4: {row[\"tof_4_mean\"]:.1f}')\n        # Thm_5\n        cv2.rectangle(img, (30, 120), (50, 140), color=color5, thickness=-1)\n        cv2.rectangle(img, (30, 120), (50, 140), color=(255, 0, 0), thickness=1)\n        img = draw_bbox(img, (30, 120, 50, 140), label=f'5: {row[\"tof_5_mean\"]:.1f}')\n\n        ims.append(img)\n\n    fig = plt.figure(figsize=(6,6))\n    plt.axis('off')\n    im = plt.imshow(ims[0].astype(np.uint8))\n\n    def animate_func(i):\n        im.set_array(ims[i].astype(np.uint8))\n        return [im]\n        \n    plt.close(fig)\n\n    return animation.FuncAnimation(fig, animate_func, frames=len(ims), interval = 1000 // 24)\n\nseq_id = np.random.choice(train_df['sequence_id'].unique())\nanim = create_tof(seq_id)\nanim","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:20.957104Z","iopub.execute_input":"2025-07-29T14:48:20.957342Z","iopub.status.idle":"2025-07-29T14:48:26.216817Z","shell.execute_reply.started":"2025-07-29T14:48:20.957322Z","shell.execute_reply":"2025-07-29T14:48:26.216076Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\nseq_list = np.random.choice(train_df[train_df['behavior_flow'] == FLOW_1]['sequence_id'].unique(), 3)\n\ntof_mean_list = ['tof_1_mean','tof_2_mean','tof_3_mean','tof_4_mean','tof_5_mean']\n\ntitles = [seq for seq in seq_list]\n\nfig = make_subplots(rows=3, cols=1, subplot_titles=titles)\n\nfor idx, seq_id in enumerate(seq_list, start=1):\n    \n    tmp = train_df[train_df['sequence_id'] == seq_id].reset_index()\n    \n    for tof, col in zip(tof_mean_list, thm_color):\n        fig.add_trace(\n            go.Scatter(\n                x = tmp.index,\n                y = tmp[tof],\n                mode = 'lines',\n                name = tof,\n                marker_color = col,\n                showlegend = (idx == 1),\n            ), idx, 1\n        )\n        \n    fig.add_vrect(\n        x0=0, x1=tmp.iloc[0]['Relaxes and moves hand to target location']-1,\n        fillcolor=\"red\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Transition\",\n        annotation_position=\"top left\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Relaxes and moves hand to target location']-1, x1=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1,\n        fillcolor=\"green\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Pause\",\n        annotation_position=\"top right\"\n    )\n    fig.add_vrect(\n        x0=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1, x1=len(tmp)-1,\n        fillcolor=\"blue\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Gesture\",\n        annotation_position=\"top right\"\n    )\nfig.update_layout(\n    title=FLOW_1,\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:26.217669Z","iopub.execute_input":"2025-07-29T14:48:26.217908Z","iopub.status.idle":"2025-07-29T14:48:26.867811Z","shell.execute_reply.started":"2025-07-29T14:48:26.217889Z","shell.execute_reply":"2025-07-29T14:48:26.867186Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ Remove Thermopile Outliers","metadata":{}},{"cell_type":"code","source":"tmp = train_df[~train_df['sequence_id'].isin(tof_na_list)].reset_index(drop=True)\n\ntmp = tmp[tmp['behavior'] == 'Hand at target location'].groupby('sequence_id').agg(\n    pause_diff_1 = ('tof_1_mean', lambda x: np.mean(np.abs(np.diff(x)))),\n    pause_diff_2 = ('tof_2_mean', lambda x: np.mean(np.abs(np.diff(x)))),\n    pause_diff_3 = ('tof_3_mean', lambda x: np.mean(np.abs(np.diff(x)))),\n    pause_diff_4 = ('tof_4_mean', lambda x: np.mean(np.abs(np.diff(x)))),\n    pause_diff_5 = ('tof_5_mean', lambda x: np.mean(np.abs(np.diff(x)))),\n    gesture = ('gesture', lambda x: x.iloc[0]),\n).reset_index()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:26.868546Z","iopub.execute_input":"2025-07-29T14:48:26.868899Z","iopub.status.idle":"2025-07-29T14:48:30.835883Z","shell.execute_reply.started":"2025-07-29T14:48:26.868879Z","shell.execute_reply":"2025-07-29T14:48:30.835322Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig = go.Figure()\n\nfor i, col in enumerate(thm_color, start=1):\n    fig.add_trace(\n        go.Box(\n            y = tmp[f'pause_diff_{i}'],\n            name = f'pause_diff_{i}',\n            marker_color = col,\n            boxmean='sd',)\n    )\n\nfig.add_hrect(\n    y0=80,\n    y1=100,\n    fillcolor=\"red\",\n    opacity=0.2,\n    layer=\"below\",\n    line_width=0\n)\n\nfig.update_layout(\n    title=\"Ditribution of TOF Diff in Pause Phase\"\n)\n\nfig.show(renderer=\"iframe\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:30.836585Z","iopub.execute_input":"2025-07-29T14:48:30.836758Z","iopub.status.idle":"2025-07-29T14:48:30.880874Z","shell.execute_reply.started":"2025-07-29T14:48:30.836744Z","shell.execute_reply":"2025-07-29T14:48:30.880329Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"seq_list = ['SEQ_047121','SEQ_030299']\n\ntof_mean_list = ['tof_1_mean','tof_2_mean','tof_3_mean','tof_4_mean','tof_5_mean']\n\ntitles = [seq for seq in seq_list]\n\nfig = go.Figure()\n\ntmp = train_df[train_df['sequence_id'] == seq_list[0]].reset_index()\n    \nfor tof, col in zip(tof_mean_list, thm_color):\n        fig.add_trace(\n            go.Scatter(\n                x = tmp.index,\n                y = tmp[tof],\n                mode = 'lines',\n                name = tof,\n                marker_color = col,\n            ),\n        )\n        \nfig.add_vrect(\n        x0=0, x1=tmp.iloc[0]['Relaxes and moves hand to target location']-1,\n        fillcolor=\"red\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Transition\",\n        annotation_position=\"top left\"\n    )\nfig.add_vrect(\n        x0=tmp.iloc[0]['Relaxes and moves hand to target location']-1, x1=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1,\n        fillcolor=\"green\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Pause\",\n        annotation_position=\"top right\"\n    )\nfig.add_vrect(\n        x0=tmp.iloc[0]['Hand at target location'] + tmp.iloc[0]['Relaxes and moves hand to target location'] -1, x1=len(tmp)-1,\n        fillcolor=\"blue\",\n        opacity=0.1,\n        line_width=0,\n        row=idx, col=1,\n        annotation_text=\"Gesture\",\n        annotation_position=\"top right\"\n    )\nfig.update_layout(\n    title=FLOW_1,\n)\n\nfig.show(renderer='iframe')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:30.881547Z","iopub.execute_input":"2025-07-29T14:48:30.881774Z","iopub.status.idle":"2025-07-29T14:48:30.969270Z","shell.execute_reply.started":"2025-07-29T14:48:30.881747Z","shell.execute_reply":"2025-07-29T14:48:30.968743Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### ✅ TOF Spectrogram\n\n    mel_spec = librsoa.feature.melspectrogram(y=x, sr=200, hop_length=len(x)//256, n_fft=1024, n_mels=128, fmin=0, fmax=20, win_length=128\n\n☑️ **`y`** is the input time series signal\n\n☑️ **`sr`** is the sampling frequency.\n\n☑️ **`hop_length`** produces image with width=len(x)/hop_length\n\n☑️ **`n_fft`** controls vertical resolution and quality of spectrogram\n\n☑️ **`n_mels`** produces image with height=n_mels\n\n☑️ **`f_min`** is smallest frequency in our spectrogram\n\n☑️ **`f_max`** is largest frequency in our spectrogram\n\n☑️ **`win_length`** controls hortizonal resolution and quality of spectrogram\n","metadata":{}},{"cell_type":"code","source":"import librosa\nfrom scipy import interpolate\n\ndef inter_signal(x):\n    original_len = len(x)\n    interp_func = interpolate.interp1d(np.linspace(0, 1, original_len), x, kind='linear')\n    x_interp = interp_func(np.linspace(0, 1, 1024))\n    return x_interp\n\ndef spectrogram_from_tof(df, seq_id, sr=64):\n    tmp = df[df['sequence_id'] == seq_id].reset_index()\n\n    img = np.zeros((256,50,5), dtype='float32')\n\n    for i, tof in enumerate(tof_mean_list):\n        x = tmp[tof].values\n        m = np.nanmean(x)\n        x = np.nan_to_num(x, nan=m)\n        x = inter_signal(x)\n\n        mel_spec = librosa.feature.melspectrogram(y=x, sr=sr, hop_length=len(x)//50, n_fft=100,\n                                                  n_mels=256, fmin=0, fmax=sr)\n        width = (mel_spec.shape[1]//10)*10\n        mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max).astype(np.float32)[:,:width]\n\n        mel_spec_db = (mel_spec_db+40)/40\n        img[:,:,i] = mel_spec_db\n\n    return img","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:30.975131Z","iopub.execute_input":"2025-07-29T14:48:30.975306Z","iopub.status.idle":"2025-07-29T14:48:30.981198Z","shell.execute_reply.started":"2025-07-29T14:48:30.975292Z","shell.execute_reply":"2025-07-29T14:48:30.980626Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"seq_list = np.random.choice(train_df['sequence_id'].unique(), 2)\n\nplt.figure(figsize=(14, 6))\n\nfor i, seq in enumerate(seq_list):\n    img = spectrogram_from_tof(train_df, seq)\n\n    for j, tof in enumerate(tof_mean_list):\n        plt.subplot(2, 5, 5*i + j + 1)\n        plt.imshow(img[...,j], aspect='auto', origin='lower', cmap='jet')\n        plt.title(f'{seq}_{tof}')\n        plt.axis('off')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:30.981950Z","iopub.execute_input":"2025-07-29T14:48:30.982163Z","iopub.status.idle":"2025-07-29T14:48:32.681646Z","shell.execute_reply.started":"2025-07-29T14:48:30.982141Z","shell.execute_reply":"2025-07-29T14:48:32.680929Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(14, 6))\n\nfor i, seq in enumerate(seq_list):\n    img = np.zeros((256,256), dtype=np.float32)\n    \n    img_tof = spectrogram_from_tof(train_df, seq)\n    img_tof = np.concatenate([img_tof[...,i] for i in range(5)], axis=1) \n\n    img[:, 3:253] = img_tof\n    \n    plt.subplot(1,2,i+1)\n    plt.imshow(img, aspect='auto', origin='lower', cmap='jet')\n    plt.axis('off')\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:32.682536Z","iopub.execute_input":"2025-07-29T14:48:32.682768Z","iopub.status.idle":"2025-07-29T14:48:33.177004Z","shell.execute_reply.started":"2025-07-29T14:48:32.682751Z","shell.execute_reply":"2025-07-29T14:48:33.176260Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 7. Data Cleaning","metadata":{}},{"cell_type":"code","source":"# Behavior\n\n## ✅ Every Sequence_id has 3 unique behavior including perform gesture\n## ✅ But Sequence_id(SEQ_011975) has only 2 unique behavior and not incldue perform gesture\n\n# Phase Ratio\n\n## ✅ gesture_ids = tmp[tmp['Gesture_Ratio'] > 85]['sequence_id'].values\n## ✅ transition_ids = tmp[tmp['Transition_Ratio'] > 95]['sequence_id'].values\n\n\n# IMU: Accelerator + Rotation\n\n## ✅ Accelerator: No Null\n## ✅ Rotation: null(0%): 8089, null(100%): 50\n## rot_nan_list(total: 50) \n\n# Thermopile Sensor\n\n## ✅ thm_1: null(0%): 8034, null(100%): 104\n## ✅ thm_2: null(0%): 8021, null(71.4%): 1, null(100%): 116\n## ✅ thm_3: null(0%): 8038, null(100%): 100\n## ✅ thm_4: null(0%): 8042, null(100%): 96\n## ✅ thm_5: null(0%): 7655, null(77.6%): 271, null(100%): 482\n\n## ✅ thm_na_list(total: 508)\n\n## ✅ thm_outlier(in pause): ['SEQ_012617']\n\n\n# Time-of-Flight Sensor\n\n## ✅ tof_1: null(0%): 8042, null(100%): 96\n## ✅ tof_2: null(0%): 8042, null(100%): 96\n## ✅ tof_3: null(0%): 8042, null(100%): 96\n## ✅ tof_4: null(0%): 8042, null(100%): 96\n## ✅ tof_5: null(0%): 7703, null(77.6%): 1, null(100%): 434\n\n## ✅ tof_na_list(total: 435)\n\n## ✅ tof_outlier(in pause): ['SEQ_047121','SEQ_030299']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:33.177856Z","iopub.execute_input":"2025-07-29T14:48:33.178108Z","iopub.status.idle":"2025-07-29T14:48:33.182121Z","shell.execute_reply.started":"2025-07-29T14:48:33.178089Z","shell.execute_reply":"2025-07-29T14:48:33.181373Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"delete_id = []\n\n# Behavior\ndelete_id.append('SEQ_011975')\n\n# Phase Ratio\ndelete_id.extend(gesture_ids.tolist())\ndelete_id.extend(transition_ids.tolist())\n\n# IMU Null\ndelete_id.extend(rot_nan_list)\n\n# THM Null\ndelete_id.extend(thm_na_list)\ndelete_id.append('SEQ_012617')\n\n# TOF Null\ndelete_id.extend(tof_na_list)\ndelete_id.extend(['SEQ_047121','SEQ_030299'])\n\ndelete_id = list(set(delete_id))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:33.182879Z","iopub.execute_input":"2025-07-29T14:48:33.183137Z","iopub.status.idle":"2025-07-29T14:48:33.196025Z","shell.execute_reply.started":"2025-07-29T14:48:33.183111Z","shell.execute_reply":"2025-07-29T14:48:33.195282Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/cmi-detect-behavior-with-sensor-data/train.csv')\n\nprint(f\"\\n{Fore.BLUE}{st_}Deleting Sequence id {len(delete_id)}\")\nprint(f\"\\n{Fore.BLUE}{st_}total {train_df['sequence_id'].nunique()} seqeunces Before Cleaning\")\n\ntrain_df = train_df[~train_df['sequence_id'].isin(delete_id)].reset_index(drop=True)\n\nprint(f\"\\n{Fore.BLUE}{st_}total {train_df['sequence_id'].nunique()} seqeunces After Cleaning\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-29T14:48:33.468824Z","iopub.execute_input":"2025-07-29T14:48:33.469091Z","iopub.status.idle":"2025-07-29T14:48:33.472718Z","shell.execute_reply.started":"2025-07-29T14:48:33.469069Z","shell.execute_reply":"2025-07-29T14:48:33.472073Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_df.to_csv('/kaggle/working/train_cleaned.csv', index=False)","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}