A study on the impact of the users’ characteristics on the performance of wearable fall detection systems
Abstract 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 si...
Guardado en:
Autores principales: | José Antonio Santoyo-Ramón, Eduardo Casilari-Pérez, José Manuel Cano-García |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/6a279c30c3344997bdddec4a8ce06856 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Are wearable devices effective for preventing and detecting falls: an umbrella review (a review of systematic reviews)
por: Daniel Joseph Warrington, et al.
Publicado: (2021) -
“”: A qualitative study exploring user and non-user's perceptions of wearable activity trackers
por: Katie Burford, et al.
Publicado: (2021) -
Wearable Detection Systems for Epileptic Seizure: A review
por: Alla Fikrat Alwindawi, et al.
Publicado: (2020) -
Evaluation of accelerometer-based fall detection algorithms on real-world falls.
por: Fabio Bagalà, et al.
Publicado: (2012) -
Performance Comparison for Single-User and Multi-User Network MIMO Cellular Systems with Power Management
por: Jeng-Shin Sheu, et al.
Publicado: (2021)