RT Conference Proceedings
T1 A cross-dataset evaluation of wearable fall detection systems
A1 Santoyo-Ramón, José Antonio
A1 Casilari-Pérez, Eduardo
A1 Cano-García, José Manuel
K1 Inteligencia artificial
K1 Dispositivos lógicos
K1 Dispositivos lógicos programables
K1 Redes neuronales (Informática)
K1 Alta tecnología
AB The popularity of wearables and their seamless integration into our daily lives have transformed these devices into an appealing resource to deploy automatic fall detection systems. During last years, a massive literature on new methods and algorithms for these wearable detectors has been produced. However, in most cases these algorithms are tested against one single (or at best two) datasets containing signals captured from falls and conventional movements. This work evaluates the behavior of a fall detection system based on a convolutional neural network when different public repositories of movements are alternatively used for training and testing the model. After a systematic cross-dataset evaluation involving four well-known datasets, we show the difficulty of extrapolating the results achieved by a certain classifier for a particular database when another dataset is considered. Results seem to indicate that classification methods tend to overlearn the particular conditions (typology of movements, characteristics of the employed sensor, experimental subjects) under which the training samples were generated.
YR 2022
FD 2022-05
LK https://hdl.handle.net/10630/24092
UL https://hdl.handle.net/10630/24092
LA eng
NO Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech.Fondos FEDER (proyecto UMA18-FEDERJA-022), Junta de Andalucía (proyecto PAIDI P18-RT-1652)
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026