RT Journal Article
T1 A study on the application of convolutional neural networks to fall detection evaluated with multiple public datasets
A1 Casilari-Pérez, Eduardo
A1 Lora Rivera, Raúl
A1 García-Lagos, Francisco
K1 Accidentes domésticos - Prevención - Efectos de las innovaciones tecnológicas
AB Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. The core of a FDS is the algorithm that discriminates falls from conventional Activities of Daily Life (ADLs). This work presents and evaluates a convolutional deep neural network when it is applied to identify fall patterns based on the measurements collected by a transportable tri-axial accelerometer. In contrast with most works in the related literature, the evaluation is performed against a wide set of public data repositories containing the traces obtained from diverse groups of volunteers during the execution of ADLs and mimicked falls. Although the method can yield very good results when it is hyper-parameterized for a certain dataset, the global evaluation with the other repositories highlights the difficulty of extrapolating to other testbeds the network architecture that was configured and optimized for a particular dataset.
PB MDPI
YR 2020
FD 2020
LK https://hdl.handle.net/10630/36634
UL https://hdl.handle.net/10630/36634
LA eng
NO Casilari, E., Lora-Rivera, R., & García-Lagos, F. (2020). A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets. Sensors, 20(5), 1466. https://doi.org/10.3390/s20051466
NO This work was supported by FEDER Funds (under grant UMA18-FEDERJA-022) and Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026