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 +1 more
2/4/2024
cs.CVcs.LGeess.SP

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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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Cite this paper

@article{martnezzarzuela2024comparative,
  title  = {A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures},
  author = {Mario Martínez-Zarzuela and David González-Ortega and Míriam Antón-Rodríguez and Francisco Javier Díaz-Pernas and Henning Müller and Cristina Simón-Martínez},
  year   = {2024},
  eprint = {2402.05958},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.05958v1}
}

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