RT Journal Article
T1 A study on the impact of the users’ characteristics on the performance of wearable fall detection systems
A1 Santoyo-Ramón, José Antonio
A1 Casilari-Pérez, Eduardo
A1 Cano-García, José Manuel
K1 Accidentes domésticos - Prevención - Efectos de las innovaciones tecnológicas
AB Wearable Fall Detection Systems (FDSs) have gained much research interest during last decade. In this regard, Machine Learning (ML) classifiers have shown great efficiency in discriminating falls and conventional movements or Activities of Daily Living (ADLs) based on the analysis of the signals captured by transportable inertial sensors. Due to the intrinsic difficulties of training and testing this type of detectors in realistic scenarios and with their target audience (older adults), FDSs are normally benchmarked against a predefined set of ADLs and emulated falls executed by volunteers in a controlled environment. In most studies, however, samples from the same experimental subjects are used to both train and evaluate the FDSs. In this work, we investigate the performance of ML-based FDS systems when the test subjects have physical characteristics (weight, height, body mass index, age, gender) different from those of the users considered for the test phase. The results seem to point out that certain divergences (weight, height) of the users of both subsets (training ad test) may hamper the effectiveness of the classifiers (a reduction of up 20% in sensitivity and of up to 5% in specificity is reported). However, it is shown that the typology of the activities included in these subgroups has much greater relevance for the discrimination capability of the classifiers (with specificity losses of up to 95% if the activity types for training and testing strongly diverge).
PB Nature Research
YR 2021
FD 2021
LK https://hdl.handle.net/10630/36620
UL https://hdl.handle.net/10630/36620
LA eng
NO Santoyo-Ramón, J.A., Casilari-Pérez, E. & Cano-García, J.M. A study on the impact of the users’ characteristics on the performance of wearable fall detection systems. Sci Rep 11, 23011 (2021). https://doi.org/10.1038/s41598-021-02537-z
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 24 ene 2026