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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2410.13605 |
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| _version_ | 1866909353508864000 |
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| author | Leite, Clayton Souza Mauranen, Henry Zhanabatyrova, Aziza Xiao, Yu |
| author_facet | Leite, Clayton Souza Mauranen, Henry Zhanabatyrova, Aziza Xiao, Yu |
| contents | Transformers have excelled in natural language processing and computer vision, paving their way to sensor-based Human Activity Recognition (HAR). Previous studies show that transformers outperform their counterparts exclusively when they harness abundant data or employ compute-intensive optimization algorithms. However, neither of these scenarios is viable in sensor-based HAR due to the scarcity of data in this field and the frequent need to perform training and inference on resource-constrained devices. Our extensive investigation into various implementations of transformer-based versus non-transformer-based HAR using wearable sensors, encompassing more than 500 experiments, corroborates these concerns. We observe that transformer-based solutions pose higher computational demands, consistently yield inferior performance, and experience significant performance degradation when quantized to accommodate resource-constrained devices. Additionally, transformers demonstrate lower robustness to adversarial attacks, posing a potential threat to user trust in HAR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13605 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transformer-Based Approaches for Sensor-Based Human Activity Recognition: Opportunities and Challenges Leite, Clayton Souza Mauranen, Henry Zhanabatyrova, Aziza Xiao, Yu Machine Learning Transformers have excelled in natural language processing and computer vision, paving their way to sensor-based Human Activity Recognition (HAR). Previous studies show that transformers outperform their counterparts exclusively when they harness abundant data or employ compute-intensive optimization algorithms. However, neither of these scenarios is viable in sensor-based HAR due to the scarcity of data in this field and the frequent need to perform training and inference on resource-constrained devices. Our extensive investigation into various implementations of transformer-based versus non-transformer-based HAR using wearable sensors, encompassing more than 500 experiments, corroborates these concerns. We observe that transformer-based solutions pose higher computational demands, consistently yield inferior performance, and experience significant performance degradation when quantized to accommodate resource-constrained devices. Additionally, transformers demonstrate lower robustness to adversarial attacks, posing a potential threat to user trust in HAR. |
| title | Transformer-Based Approaches for Sensor-Based Human Activity Recognition: Opportunities and Challenges |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.13605 |