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arXiv · 2402.05958

A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures

Mario Martínez-Zarzuela·David González-Ortega·Míriam Antón-Rodríguez·Francisco Javier Díaz-Pernas·Henning Müller·Cristina Simón-Martínez
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Abstract

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.

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Mario Martínez-Zarzuela, David González-Ortega, Míriam Antón-Rodríguez, Francisco Javier Díaz-Pernas, Henning Müller, Cristina Simón-Martínez. 2024-02-04. A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures. https://doi.org/10.1016/j.gaitpost.2023.07.149

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