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

Autor:	Martínez-Zarzuela, Mario, González-Ortega, David, Antón-Rodríguez, Míriam, Díaz-Pernas, Francisco Javier, Müller, Henning, Simón-Martínez, Cristina
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Zdroj:	Gait & Posture (2023) 106, p. 119-120
Druh dokumentu:	Working Paper
DOI:	10.1016/j.gaitpost.2023.07.149
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.05958 Zobrazit plný text záznamu View this record from Arxiv