Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916647112015872 |
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| author | Bian, Sizhen Rey, Vitor Fortes Yuan, Siyu Lukowicz, Paul |
| author_facet | Bian, Sizhen Rey, Vitor Fortes Yuan, Siyu Lukowicz, Paul |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification Bian, Sizhen Rey, Vitor Fortes Yuan, Siyu Lukowicz, Paul Signal Processing 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}. |
| title | Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.06311 |