RT Journal Article
T1 A study of one-class classification algorithms for wearable fall sensors
A1 Santoyo Ramon, José Antonio
A1 Casilari-Pérez, Eduardo
A1 Cano-García, José Manuel
K1 Accidentes domésticos - Prevención - Efectos de las innovaciones tecnológicas
AB In recent years, the popularity of wearable devices has fostered the investigation of automatic fall detection systems based on the analysis of the signals captured by transportable inertial sensors. Due to the complexity and variety of human movements, the detection algorithms that offer the best performance when discriminating falls from conventional Activities of Daily Living (ADLs) are those built on machine learning and deep learning mechanisms. In this regard, supervised machine learning binary classification methods have been massively employed by the related literature. However, the learning phase of these algorithms requires mobility patterns caused by falls, which are very difficult to obtain in realistic application scenarios. An interesting alternative is offered by One-Class Classifiers (OCCs), which can be exclusively trained and configured with movement traces of a single type (ADLs). In this paper, a systematic study of the performance of various typical OCCs (for diverse sets of input features and hyperparameters) is performed when applied to nine public repositories of falls and ADLs. The results show the potentials of these classifiers, which are capable of achieving performance metrics very similar to those of supervised algorithms (with values for the specificity and the sensitivity higher than 95%). However, the study warns of the need to have a wide variety of types of ADLs when training OCCs, since activities with a high degree of mobility can significantly increase the frequency of false alarms (ADLs identified as falls) if not considered in the data subsets used for training.
PB MDPI
YR 2021
FD 2021
LK https://hdl.handle.net/10630/36624
UL https://hdl.handle.net/10630/36624
LA eng
NO Santoyo-Ramón, J. A., Casilari, E., & Cano-García, J. M. (2021). A Study of One-Class Classification Algorithms for Wearable Fall Sensors. Biosensors, 11(8), 284. https://doi.org/10.3390/bios11080284
NO This research was funded by FEDER Funds (under grant UMA18-FEDERJA-022), Andalusian Regional Government (-Junta de Andalucía- grant PAIDI P18-RT-1652) and Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026