Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification
Year: 2025
Type: preprint
Abstract: While human body capacitance ($HBC$) has been explored as a novel wearable motion sensing modality, its competence has never been quantitatively demonstrated compared to that of the dominant inertial measurement unit ($IMU$) in practical scenarios. This work is thus motivated to evaluate the contribution of $HBC$ in wearable motion sensing. A real-life case study, gym workout tracking, is described to assess the effectiveness of $HBC$ as a complement to $IMU$ in activity recognition. Fifty gym sessions from ten volunteers were collected, bringing a fifty-hour annotated $IMU$ and $HBC$ dataset. With a hybrid CNN-Dilated neural network model empowered with the self-attention mechanism, $HBC$ slightly improves accuracy to the $IMU$ for workout recognition and has substantial advantages over $IMU$ for repetition counting. This work helps to enhance the understanding of $HBC$, a novel wearable motion-sensing modality based on the body-area electrostatic field. All materials presented in this work are open-sourced to promote further study \footnote{https://github.com/zhaxidele/Toolkit-for-HBC-sensing}.
Source: arXiv (Cornell University)
Authors: Bian, Sizhen, Rey, Vitor Fortes, Yuan, Siyu, Lukowicz, Paul
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979248068
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