RT Conference Proceedings
T1 Evaluation of a Fall Alerting System based on a Convolutional Deep Neural Network
A1 Casilari-Pérez, Eduardo
A1 Lora Rivera, Raúl
A1 García-Lagos, Francisco
K1 Redes Neuronales (Informática)
AB Owing to the effects of falls on quality of life of the elderly, automatic fall detection systems (FDS) have become a key research topic in the ambit of telecare. This works assesses the performance of convolutional neural networks when they are applied to identify fall accidents in a wearable FDS provided with a tri-axial accelerometer. The evaluation of the detection algorithm is carried out by employing a benchmarking repository with a wide set of traces captured from a wide group of volunteers that executed a programmed series of Activities of the Daily Living (ADLs) and emulated falls. Results show that the CNN can properly distinguish both types of movements with a success rate (specificity and sensitivity) around 99%.
YR 2019
FD 2019-03-04
LK https://hdl.handle.net/10630/17400
UL https://hdl.handle.net/10630/17400
LA eng
NO Artículo sobre detección de caídas con redes neuronales profundas
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026