![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F5210536%2F82deaef3694bdc3b6a1e573c14ab5410%2FScreenshot%202023-09-11%20at%2022.11.41.png?generation=1694450569848772&alt=media\")
can someone explain the features in the data ?
\nWhat exactly is \"anglez\" and \"enmo\"?
\nI found their definitions and formulae but couldn't understand how are they useful in prediction and why exactly are they calculated?
can someone explain the features in the data ?
\nWhat exactly is \"anglez\" and \"enmo\"?
\nI found their definitions and formulae but couldn't understand how are they useful in prediction and why exactly are they calculated?
gives ideas to use
", "rawMarkdown": "gives ideas to use", "votes": 3 }, { "id": 2431986, "postDate": "2023-09-10T14:38:52.023Z", "content": "Hi Chaudari take a look on those Kaggle Notebooks to understand Z-Angle and Euclidean Norm Minus One:
\nZzzs: Plots of all 277 anglez and enmo series - By Carl McBride Ellis
\nhttps://www.kaggle.com/code/carlmcbrideellis/zzzs-plots-of-all-277-anglez-and-enmo-series
CMI Sleep Visualization - By Connor
\nhttps://www.kaggle.com/code/clane9/cmi-sleep-visualization
Here's a good post @mpwolke that gives you an idea of how to actually use it. Please refer.
", "rawMarkdown": "[Here's a good post @mpwolke](https://www.kaggle.com/competitions/child-mind-institute-detect-sleep-states/discussion/437261) that gives you an idea of how to actually use it. Please refer.\n", "votes": 4 }, { "id": 2434786, "postDate": "2023-09-12T14:06:05.610Z", "content": "Here is the code for both
\nanglez:
\nhttps://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L147
https://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L299
\n\nsf: seems to be sample frequency
", "rawMarkdown": "Here is the code for both\n\nanglez:\nhttps://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L147\n\nhttps://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L299\n\nenmo:\nhttps://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L304-L309\n\nsf: seems to be sample frequency", "votes": 2 }, { "id": 2433539, "postDate": "2023-09-11T16:43:14.043Z", "content": "Also, found the forumla for their calculation in the documentation of GGIR here:
\nhttps://cran.r-project.org/web/packages/GGIR/GGIR.pdf
![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F5210536%2Fd8be50a13b12d39f41f7bd110fc4260d%2FScreenshot%202023-09-11%20at%2022.12.00.png?generation=1694450583001776&alt=media\")
The formula for anglez there is acc_z / (acc_x^2 + acc_y^2), but I suppose it is missing a square-root. It is present at the Tommy's link https://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L147.
Asked ChatGPT and below is the output: https://chat.openai.com/c/59e6baf6-384e-4684-9bd9-98efd7360704
\n\n\nUsing the context below, explain anglez
\n
\n anglez - As calculated and described by the GGIR package, z-angle is a metric derived from individual accelerometer components that is commonly used in sleep detection, and refers to the angle of the arm relative to the vertical axis of the body
ChatGPT
\n\n\nIn the context provided, \"anglez\" refers to a metric used in the field of sleep detection, particularly in the context of analyzing data collected from accelerometers. The term \"anglez\" is a shorthand way of referring to the \"z-angle,\" which is a specific measurement calculated and described by the GGIR package.
\nThe z-angle is a metric derived from the individual accelerometer components. Accelerometers are sensors that measure acceleration and can be used to track movements and changes in position. In this case, the z-angle is calculated based on the accelerometer data and provides information about the angle of the arm relative to the vertical axis of the body.
\nIn simpler terms, it helps determine the orientation of a person's arm concerning whether it is aligned with the vertical axis (straight up and down) or at some other angle. This information can be valuable in sleep detection studies, as it may help researchers understand the position and movement of a person's arm during sleep, which can be relevant for assessing sleep quality or sleep patterns.
\n
\n\nUsing the context below, explain enmo
\n
\n enmo - As calculated and described by the GGIR package, ENMO is the Euclidean Norm Minus One of all accelerometer signals, with negative values rounded to zero. While no standard measure of acceleration exists in this space, this is one of the several commonly computed features
ChatGPT
\n\n", "rawMarkdown": "Asked ChatGPT and below is the output: https://chat.openai.com/c/59e6baf6-384e-4684-9bd9-98efd7360704\n\n## anglez\n\n> Using the context below, explain anglez\n> anglez - As calculated and described by the GGIR package, z-angle is a metric derived from individual accelerometer components that is commonly used in sleep detection, and refers to the angle of the arm relative to the vertical axis of the body\n\nChatGPT\n> In the context provided, \"anglez\" refers to a metric used in the field of sleep detection, particularly in the context of analyzing data collected from accelerometers. The term \"anglez\" is a shorthand way of referring to the \"z-angle,\" which is a specific measurement calculated and described by the GGIR package.\n>\n> The z-angle is a metric derived from the individual accelerometer components. Accelerometers are sensors that measure acceleration and can be used to track movements and changes in position. In this case, the z-angle is calculated based on the accelerometer data and provides information about the angle of the arm relative to the vertical axis of the body.\n>\n> In simpler terms, it helps determine the orientation of a person's arm concerning whether it is aligned with the vertical axis (straight up and down) or at some other angle. This information can be valuable in sleep detection studies, as it may help researchers understand the position and movement of a person's arm during sleep, which can be relevant for assessing sleep quality or sleep patterns.\n\n\n## enmo\n\n> Using the context below, explain enmo\n> enmo - As calculated and described by the GGIR package, ENMO is the Euclidean Norm Minus One of all accelerometer signals, with negative values rounded to zero. While no standard measure of acceleration exists in this space, this is one of the several commonly computed features\n\nChatGPT\n\n> In the provided context, \"enmo\" stands for \"ENMO,\" which stands for \"Euclidean Norm Minus One.\" ENMO is a metric used in data analysis, particularly in the context of accelerometer data, and it is described and calculated by the GGIR package.\n> \n> ENMO is derived from accelerometer signals, which are measurements of acceleration over time. The calculation of ENMO involves taking the Euclidean norm of all accelerometer signals and then subtracting one. The Euclidean norm, in this context, refers to the square root of the sum of the squares of each individual acceleration component. In simpler terms, it combines information from all the accelerometer axes (e.g., X, Y, and Z) into a single scalar value.\n> \n> The subtraction of one (minus one) is typically performed to adjust the ENMO values, especially when negative values are present. Negative values are often rounded to zero in this calculation, which makes sense because acceleration typically doesn't go below zero in physical measurements.\n> \n> ENMO, despite not having a standard unit of measurement like meters per second squared (m/s²) for acceleration, is one of the commonly computed features when analyzing accelerometer data. It serves as a summary metric that can help researchers assess the overall level or intensity of movement or acceleration recorded by the accelerometer sensors. Researchers may use ENMO to study physical activity levels, assess movement patterns, or investigate various health-related factors, especially in fields like sports science, health monitoring, or sleep research.", "votes": 3, "isDeleted": true } ], "comments": [ { "id": 2431666, "author_name": "mayar mohamed swilam", "author_url": "", "post_date": "2023-09-10T09:45:14.460000", "content": "In the provided context, \"enmo\" stands for \"ENMO,\" which stands for \"Euclidean Norm Minus One.\" ENMO is a metric used in data analysis, particularly in the context of accelerometer data, and it is described and calculated by the GGIR package.
\nENMO is derived from accelerometer signals, which are measurements of acceleration over time. The calculation of ENMO involves taking the Euclidean norm of all accelerometer signals and then subtracting one. The Euclidean norm, in this context, refers to the square root of the sum of the squares of each individual acceleration component. In simpler terms, it combines information from all the accelerometer axes (e.g., X, Y, and Z) into a single scalar value.
\nThe subtraction of one (minus one) is typically performed to adjust the ENMO values, especially when negative values are present. Negative values are often rounded to zero in this calculation, which makes sense because acceleration typically doesn't go below zero in physical measurements.
\nENMO, despite not having a standard unit of measurement like meters per second squared (m/s²) for acceleration, is one of the commonly computed features when analyzing accelerometer data. It serves as a summary metric that can help researchers assess the overall level or intensity of movement or acceleration recorded by the accelerometer sensors. Researchers may use ENMO to study physical activity levels, assess movement patterns, or investigate various health-related factors, especially in fields like sports science, health monitoring, or sleep research.
\n
gives ideas to use
", "votes": 3, "replies": [] }, { "id": 2431986, "author_name": "Marília Prata", "author_url": "", "post_date": "2023-09-10T14:38:52.023000", "content": "Hi Chaudari take a look on those Kaggle Notebooks to understand Z-Angle and Euclidean Norm Minus One:
\nZzzs: Plots of all 277 anglez and enmo series - By Carl McBride Ellis
\nhttps://www.kaggle.com/code/carlmcbrideellis/zzzs-plots-of-all-277-anglez-and-enmo-series
CMI Sleep Visualization - By Connor
\nhttps://www.kaggle.com/code/clane9/cmi-sleep-visualization
Here's a good post @mpwolke that gives you an idea of how to actually use it. Please refer.
", "votes": 4, "replies": [] }, { "id": 2434786, "author_name": "Tommy Vercetti", "author_url": "", "post_date": "2023-09-12T14:06:05.610000", "content": "Here is the code for both
\nanglez:
\nhttps://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L147
https://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L299
\n\nsf: seems to be sample frequency
", "votes": 2, "replies": [] }, { "id": 2433539, "author_name": "Bhargav Kowshik", "author_url": "", "post_date": "2023-09-11T16:43:14.043000", "content": "Also, found the forumla for their calculation in the documentation of GGIR here:
\nhttps://cran.r-project.org/web/packages/GGIR/GGIR.pdf
The formula for anglez there is acc_z / (acc_x^2 + acc_y^2), but I suppose it is missing a square-root. It is present at the Tommy's link https://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L147.
Asked ChatGPT and below is the output: https://chat.openai.com/c/59e6baf6-384e-4684-9bd9-98efd7360704
\n\n\nUsing the context below, explain anglez
\n
\n anglez - As calculated and described by the GGIR package, z-angle is a metric derived from individual accelerometer components that is commonly used in sleep detection, and refers to the angle of the arm relative to the vertical axis of the body
ChatGPT
\n\n\nIn the context provided, \"anglez\" refers to a metric used in the field of sleep detection, particularly in the context of analyzing data collected from accelerometers. The term \"anglez\" is a shorthand way of referring to the \"z-angle,\" which is a specific measurement calculated and described by the GGIR package.
\nThe z-angle is a metric derived from the individual accelerometer components. Accelerometers are sensors that measure acceleration and can be used to track movements and changes in position. In this case, the z-angle is calculated based on the accelerometer data and provides information about the angle of the arm relative to the vertical axis of the body.
\nIn simpler terms, it helps determine the orientation of a person's arm concerning whether it is aligned with the vertical axis (straight up and down) or at some other angle. This information can be valuable in sleep detection studies, as it may help researchers understand the position and movement of a person's arm during sleep, which can be relevant for assessing sleep quality or sleep patterns.
\n
\n\nUsing the context below, explain enmo
\n
\n enmo - As calculated and described by the GGIR package, ENMO is the Euclidean Norm Minus One of all accelerometer signals, with negative values rounded to zero. While no standard measure of acceleration exists in this space, this is one of the several commonly computed features
ChatGPT
\n\n", "votes": 3, "replies": [] } ], "raw_markdown_by_id": { "2431426": "can someone explain the features in the data ?\nWhat exactly is \"anglez\" and \"enmo\"?\nI found their definitions and formulae but couldn't understand how are they useful in prediction and why exactly are they calculated?", "2431666": "gives ideas to use", "2431986": "Hi Chaudari take a look on those Kaggle Notebooks to understand Z-Angle and Euclidean Norm Minus One:\n\nZzzs: Plots of all 277 anglez and enmo series - By Carl McBride Ellis \nhttps://www.kaggle.com/code/carlmcbrideellis/zzzs-plots-of-all-277-anglez-and-enmo-series\n\nCMI Sleep Visualization - By Connor\nhttps://www.kaggle.com/code/clane9/cmi-sleep-visualization", "2431460": "[Here's a good post @mpwolke](https://www.kaggle.com/competitions/child-mind-institute-detect-sleep-states/discussion/437261) that gives you an idea of how to actually use it. Please refer.\n", "2434786": "Here is the code for both\n\nanglez:\nhttps://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L147\n\nhttps://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L299\n\nenmo:\nhttps://github.com/cran/GGIR/blob/9bbd125367beeef0b6d9c88b5559fcd7b502e465/R/g.applymetrics.R#L304-L309\n\nsf: seems to be sample frequency", "2433539": "Also, found the forumla for their calculation in the documentation of GGIR here:\nhttps://cran.r-project.org/web/packages/GGIR/GGIR.pdf\n\n\n\n", "2433536": "Asked ChatGPT and below is the output: https://chat.openai.com/c/59e6baf6-384e-4684-9bd9-98efd7360704\n\n## anglez\n\n> Using the context below, explain anglez\n> anglez - As calculated and described by the GGIR package, z-angle is a metric derived from individual accelerometer components that is commonly used in sleep detection, and refers to the angle of the arm relative to the vertical axis of the body\n\nChatGPT\n> In the context provided, \"anglez\" refers to a metric used in the field of sleep detection, particularly in the context of analyzing data collected from accelerometers. The term \"anglez\" is a shorthand way of referring to the \"z-angle,\" which is a specific measurement calculated and described by the GGIR package.\n>\n> The z-angle is a metric derived from the individual accelerometer components. Accelerometers are sensors that measure acceleration and can be used to track movements and changes in position. In this case, the z-angle is calculated based on the accelerometer data and provides information about the angle of the arm relative to the vertical axis of the body.\n>\n> In simpler terms, it helps determine the orientation of a person's arm concerning whether it is aligned with the vertical axis (straight up and down) or at some other angle. This information can be valuable in sleep detection studies, as it may help researchers understand the position and movement of a person's arm during sleep, which can be relevant for assessing sleep quality or sleep patterns.\n\n\n## enmo\n\n> Using the context below, explain enmo\n> enmo - As calculated and described by the GGIR package, ENMO is the Euclidean Norm Minus One of all accelerometer signals, with negative values rounded to zero. While no standard measure of acceleration exists in this space, this is one of the several commonly computed features\n\nChatGPT\n\n> In the provided context, \"enmo\" stands for \"ENMO,\" which stands for \"Euclidean Norm Minus One.\" ENMO is a metric used in data analysis, particularly in the context of accelerometer data, and it is described and calculated by the GGIR package.\n> \n> ENMO is derived from accelerometer signals, which are measurements of acceleration over time. The calculation of ENMO involves taking the Euclidean norm of all accelerometer signals and then subtracting one. The Euclidean norm, in this context, refers to the square root of the sum of the squares of each individual acceleration component. In simpler terms, it combines information from all the accelerometer axes (e.g., X, Y, and Z) into a single scalar value.\n> \n> The subtraction of one (minus one) is typically performed to adjust the ENMO values, especially when negative values are present. Negative values are often rounded to zero in this calculation, which makes sense because acceleration typically doesn't go below zero in physical measurements.\n> \n> ENMO, despite not having a standard unit of measurement like meters per second squared (m/s²) for acceleration, is one of the commonly computed features when analyzing accelerometer data. It serves as a summary metric that can help researchers assess the overall level or intensity of movement or acceleration recorded by the accelerometer sensors. Researchers may use ENMO to study physical activity levels, assess movement patterns, or investigate various health-related factors, especially in fields like sports science, health monitoring, or sleep research." } }In the provided context, \"enmo\" stands for \"ENMO,\" which stands for \"Euclidean Norm Minus One.\" ENMO is a metric used in data analysis, particularly in the context of accelerometer data, and it is described and calculated by the GGIR package.
\nENMO is derived from accelerometer signals, which are measurements of acceleration over time. The calculation of ENMO involves taking the Euclidean norm of all accelerometer signals and then subtracting one. The Euclidean norm, in this context, refers to the square root of the sum of the squares of each individual acceleration component. In simpler terms, it combines information from all the accelerometer axes (e.g., X, Y, and Z) into a single scalar value.
\nThe subtraction of one (minus one) is typically performed to adjust the ENMO values, especially when negative values are present. Negative values are often rounded to zero in this calculation, which makes sense because acceleration typically doesn't go below zero in physical measurements.
\nENMO, despite not having a standard unit of measurement like meters per second squared (m/s²) for acceleration, is one of the commonly computed features when analyzing accelerometer data. It serves as a summary metric that can help researchers assess the overall level or intensity of movement or acceleration recorded by the accelerometer sensors. Researchers may use ENMO to study physical activity levels, assess movement patterns, or investigate various health-related factors, especially in fields like sports science, health monitoring, or sleep research.
\n