A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures
<|reference_start|>A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures: 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.<|reference_end|>
@article{martínez-zarzuela2024a,
    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 Mart'inez-Zarzuela, David Gonz'alez-Ortega, M'iriam
  Ant'on-Rodr'iguez, Francisco Javier D'iaz-Pernas, Henning M"uller,
  Cristina Sim'on-Mart'inez},
    journal={Gait & Posture (2023) 106, p. 119-120},
    year={2024},
    doi={10.1016/j.gaitpost.2023.07.149},
    archivePrefix={arXiv},
    eprint={2402.05958},
    primaryClass={cs.CV cs.LG eess.SP}
}