A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911773816258560 |
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| author | Martínez-Zarzuela, Mario González-Ortega, David Antón-Rodríguez, Míriam Díaz-Pernas, Francisco Javier Müller, Henning Simón-Martínez, Cristina |
| author_facet | Martínez-Zarzuela, Mario González-Ortega, David Antón-Rodríguez, Míriam Díaz-Pernas, Francisco Javier Müller, Henning Simón-Martínez, Cristina |
| contents | The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings. Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab settings, it is necessary to use a reduced number of devices for movement acquisition. Promising solutions in this context are IMU-based wearables and single camera systems. Additionally, the development of machine learning systems able to recognize and digest clinically relevant data in-the-wild is needed, and therefore determining the ideal input to those is crucial. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05958 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures Martínez-Zarzuela, Mario González-Ortega, David Antón-Rodríguez, Míriam Díaz-Pernas, Francisco Javier Müller, Henning Simón-Martínez, Cristina Computer Vision and Pattern Recognition Machine Learning Signal Processing The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings. Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab settings, it is necessary to use a reduced number of devices for movement acquisition. Promising solutions in this context are IMU-based wearables and single camera systems. Additionally, the development of machine learning systems able to recognize and digest clinically relevant data in-the-wild is needed, and therefore determining the ideal input to those is crucial. |
| title | A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures |
| topic | Computer Vision and Pattern Recognition Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2402.05958 |