\n
![\"exsample](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F22146004%2Ff08cf3725e89e6a2edfbf301653ccb16%2Fimage.png?generation=1749109260856035&alt=media\")
| Region of Fluctuation | \nPossible Meaning | \n
|---|---|
High acc_mag + High rot_angle | \nComplex gesture, likely a fast and intricate movement (e.g., \"wave hello\") | \n
High acc_mag + Low rot_angle | \nLinear fast motion with little directional change (e.g., \"slapping\") | \n
Low acc_mag + High rot_angle | \nSlow but rotational pose adjustment (e.g., \"wrist rotation\") | \n
Low acc_mag + Low rot_angle | \nStillness or pause phase (e.g., \"pause\", \"preparation pose\") | \n
acc_mag, rot_angle refer to the variables shown in @tarundirector great EDA notebook
\nHere's the example visualizaition in his work.
\n
The synergy of acc_mag and rot_angle enables interpretable distinctions between motion types. Combining their fluctuations with phase segmentation, derivatives, and demographic context helps create robust gesture classifiers—even with limited training sequences. Feel free to drop more FE ideas in the comments — Always happy to learn from others in the community!
", "messages": [ { "id": 3217606, "postDate": "2025-06-05T07:44:01.017Z", "content": "| Region of Fluctuation | \nPossible Meaning | \n
|---|---|
High acc_mag + High rot_angle | \nComplex gesture, likely a fast and intricate movement (e.g., \"wave hello\") | \n
High acc_mag + Low rot_angle | \nLinear fast motion with little directional change (e.g., \"slapping\") | \n
Low acc_mag + High rot_angle | \nSlow but rotational pose adjustment (e.g., \"wrist rotation\") | \n
Low acc_mag + Low rot_angle | \nStillness or pause phase (e.g., \"pause\", \"preparation pose\") | \n
acc_mag, rot_angle refer to the variables shown in @tarundirector great EDA notebook
\nHere's the example visualizaition in his work.
\n
The synergy of acc_mag and rot_angle enables interpretable distinctions between motion types. Combining their fluctuations with phase segmentation, derivatives, and demographic context helps create robust gesture classifiers—even with limited training sequences. Feel free to drop more FE ideas in the comments — Always happy to learn from others in the community!
", "rawMarkdown": "| Region of Fluctuation | Possible Meaning |\n| --------------------------------- | -------------------------------------------------------------------------- |\n| High `acc_mag` + High `rot_angle` | Complex gesture, likely a fast and intricate movement (e.g., \"wave hello\") |\n| High `acc_mag` + Low `rot_angle` | Linear fast motion with little directional change (e.g., \"slapping\") |\n| Low `acc_mag` + High `rot_angle` | Slow but rotational pose adjustment (e.g., \"wrist rotation\") |\n| Low `acc_mag` + Low `rot_angle` | Stillness or pause phase (e.g., \"pause\", \"preparation pose\") |\n\n\n\nacc_mag, rot_angle refer to the variables shown in [@tarundirector great EDA notebook](https://www.kaggle.com/code/tarundirector/sensor-pulse-viz-eda-for-bfrb-detection)\n\nHere's the example visualizaition in his work.\n\n\nThe synergy of acc_mag and rot_angle enables interpretable distinctions between motion types. Combining their fluctuations with phase segmentation, derivatives, and demographic context helps create robust gesture classifiers—even with limited training sequences. Feel free to drop more FE ideas in the comments — Always happy to learn from others in the community!\n\n", "votes": 15 } ], "comments": [], "raw_markdown_by_id": { "3217606": "| Region of Fluctuation | Possible Meaning |\n| --------------------------------- | -------------------------------------------------------------------------- |\n| High `acc_mag` + High `rot_angle` | Complex gesture, likely a fast and intricate movement (e.g., \"wave hello\") |\n| High `acc_mag` + Low `rot_angle` | Linear fast motion with little directional change (e.g., \"slapping\") |\n| Low `acc_mag` + High `rot_angle` | Slow but rotational pose adjustment (e.g., \"wrist rotation\") |\n| Low `acc_mag` + Low `rot_angle` | Stillness or pause phase (e.g., \"pause\", \"preparation pose\") |\n\n\n\nacc_mag, rot_angle refer to the variables shown in [@tarundirector great EDA notebook](https://www.kaggle.com/code/tarundirector/sensor-pulse-viz-eda-for-bfrb-detection)\n\nHere's the example visualizaition in his work.\n\n\nThe synergy of acc_mag and rot_angle enables interpretable distinctions between motion types. Combining their fluctuations with phase segmentation, derivatives, and demographic context helps create robust gesture classifiers—even with limited training sequences. Feel free to drop more FE ideas in the comments — Always happy to learn from others in the community!\n\n" } }