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IMU Fitness

Basic Dataset

An open-source synthetic IMU dataset for wearable fitness applications.

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The IMU Fitness Basic dataset contains 2,000 IMU samples (plus paired videos) of avatars doing several reps of 10 common exercises. This IMU (3D sensor orientation)  dataset can be used to develop and/or validate algorithms from wearable wrist-worn sensors, such as the Apple Watch. The dataset includes massive variation in wrist offsets, rep truncations, avatar body shapes, movement trajectories, and more. From rep cadence to kinematic trajectories, avatars perform each rep slightly differently (just like real humans).


<li>2,000 paired IMU (3D sensor orientation) samples + videos</li>
<li>20 fps datasets</li>
<li>10 exercises</li>
<li>Lifelike rep behavior (no two reps are the same)</li>
<li>Widespread kinematic variation, rep truncations, and cadence</li>
<li>Avatars with diverse heights and body shapes</li>


<li>Arm Raises (with dumbbells)</li>
<li>Bicep Curls (with dumbbells)</li>
<li>Bird Dogs</li>
<li>Hammer Curls (with dumbbells)</li>
<li>Overhead Presses (with dumbbells)</li>
<li>Tricep Kickbacks Bent Over (single-sided, both L/R arms, with dumbbell)</li>
<li>V Ups</li>


The paired IMU (3D sensor orientation) and video data is accompanied by a rich set of per-frame perfect labels and metadata. For more precise descriptions, please visit our <a href="" target="_blank">README</a>.
A few highlights:
<li>Frame-specific rep counts.</li>
<li>IMU sensor readings (3D sensor orientation represented by rotation matrices) at 20fps.</li>
<li>Wrist location and crown orientation of the IMU sensor on the avatar.</li>
<li>Relative xy reference and wrist rotation of the IMU sensor.</li>
<li>Amount of kinematic variation and speed variation injected into each rep.</li>


480 x 480 mp4 videos (20fps) with paired IMU readings (20 fps)
Total size: 7.1GB
Each exercise is in a different zipped folder: 300MB - 2.2GB


This dataset is licensed under a <a href="" target="_blank">Creative Commons Attribution 4.0 International License</a>.
Both academic and commercial applications are allowed.


At Infinity AI, we use rotation matrices to represent IMU sensor rotations in 3D space. If you’re not familiar with rotation matrices, they can be easily converted to eulers or quaternions using scipy (example notebook, scipy reference).
This synthetic IMU (3D sensor orientation) data has been validated against that produced by the Apple CoreMotion attitude measurements (e.g. Apple Watch).
Note: Raw accelerometry and gyroscope data streams are not provided, only 3D sensor orientation data (also referred to as angular position, rotation vector, “quaternions,” etc.).


<li>Github <a href="" target="_blank">README</a>: full dataset and annotation descriptions</li>
<li>Demo <a href="" target="_blank">Jupyter notebook</a></li>

Questions? We’re happy to chat asynchronously via email or hop on a call. Just send us a note at (this goes to all of the Infinity AI founders).

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