Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909540209917952 |
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| author | Calatrava-Nicolás, Francisco M. Miyauchi, Shoko Mozos, Oscar Martinez |
| author_facet | Calatrava-Nicolás, Francisco M. Miyauchi, Shoko Mozos, Oscar Martinez |
| contents | We present a new adversarial deep learning framework for the problem of human activity recognition (HAR) using inertial sensors worn by people. Our framework incorporates a novel adversarial activity-based discrimination task that addresses inter-person variability-i.e., the fact that different people perform the same activity in different ways. Overall, our proposed framework outperforms previous approaches on three HAR datasets using a leave-one-(person)-out cross-validation (LOOCV) benchmark. Additional results demonstrate that our discrimination task yields better classification results compared to previous tasks within the same adversarial framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12819 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition Calatrava-Nicolás, Francisco M. Miyauchi, Shoko Mozos, Oscar Martinez Signal Processing Computer Vision and Pattern Recognition Machine Learning We present a new adversarial deep learning framework for the problem of human activity recognition (HAR) using inertial sensors worn by people. Our framework incorporates a novel adversarial activity-based discrimination task that addresses inter-person variability-i.e., the fact that different people perform the same activity in different ways. Overall, our proposed framework outperforms previous approaches on three HAR datasets using a leave-one-(person)-out cross-validation (LOOCV) benchmark. Additional results demonstrate that our discrimination task yields better classification results compared to previous tasks within the same adversarial framework. |
| title | Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition |
| topic | Signal Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.12819 |