RT Journal Article
T1 Cross-dataset evaluation of wearable fall detection systems using data from real falls and long-term monitoring of daily life
A1 Silva, Carlos A.
A1 Casilari-Pérez, Eduardo
A1 García-Bermúdez, Rodolfo
K1 Detectores
K1 Aprendizaje automático (Inteligencia artificial)
AB The evaluation of fall detection systems based on wearables is controversial as most studies in the literature benchmark their proposals against falls that are simulated by experimental subjects under unrealistic laboratory conditions. In order to systematically investigate the suitability of this procedure, this paper evaluates a wide set of artificial intelligence algorithms used for fall detection, when trained with a large number of datasets containing acceleration samples captured during the emulation of falls and ordinary movements and then tested with the signals of both actual falls and long-term traces collected from the constant monitoring of users during their daily routines. The results, based on a large number of repositories, show a remarkable degradation in all performance metrics (sensitivity, specificity and false alarm hourly rate) with respect to the typical case in which the detectors are tested with the same types of laboratory movements for which they were trained.
PB Elsevier
YR 2024
FD 2024
LK https://hdl.handle.net/10630/31456
UL https://hdl.handle.net/10630/31456
LA eng
NO Carlos A. Silva, Eduardo Casilari, Rodolfo García-Bermúdez, Cross-dataset evaluation of wearable fall detection systems using data from real falls and long-term monitoring of daily life, Measurement, Volume 235, 2024, 114992, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2024.114992.
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026