RT Journal Article
T1 A cross-dataset deep learning-based classifier for people fall detection and identification
A1 Delgado-Escaño, Rubén
A1 Castro, Francisco M.
A1 Ramos-Cózar, Julián
A1 Marín-Jiménez, Manuel J.
A1 Guil-Mata, Nicolás
A1 Casilari-Pérez, Eduardo
K1 Ancianos - Protección, asistencia, etc. - Innovaciones tecnológicas
AB This paper addresses the issue of fall detection, particularly for elderly individuals who may live alone and be unable to call for help after a fall. The objective is to develop a deep learning-based approach that can detect falls and identify individuals without needing model fine-tuning for different datasets. The proposed method uses a multi-task learning model that processes raw inertial data to simultaneously detect falls and identify people. The model achieves over 98% accuracy in fall detection across four datasets, with less than 1.6% false positives, and identifies people with an average accuracy of 79.6%. It operates in real-time, requiring no retraining for new subjects, making it suitable for practical implementation.
PB Elsevier
YR 2020
FD 2020
LK https://hdl.handle.net/10630/32673
UL https://hdl.handle.net/10630/32673
LA eng
NO Rubén Delgado-Escaño, Francisco M. Castro, Julián R. Cózar, Manuel J. Marín-Jiménez, Nicolás Guil, Eduardo Casilari, A cross-dataset deep learning-based classifier for people fall detection and identification, Computer Methods and Programs in Biomedicine, Volume 184, 2020, 105265, ISSN 0169-2607, https://doi.org/10.1016/j.cmpb.2019.105265. (https://www.sciencedirect.com/science/article/pii/S0169260719311770)
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026