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

• URL: http://arxiv.org/abs/2402.05958v1
• Date: Sun, 4 Feb 2024 19:45:59 GMT
• Title: A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures
• Authors: 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
• Abstract summary: High-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings. 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. The development of machine learning systems able to recognize and digest clinically relevant data in-the-wild is needed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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