Automatic fall detection will reduce the consequences of falls in the elderly and promote independent living, ensuring people can confidently live safely at home. Inertial sensor technology can distinguish falls from normal activities. However, <7% of studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events. We have extracted temporal and kinematic parameters to further improve the development of fall detection algorithms. A total of 100 real-world falls were analysed. Subjects with a known fall history were recruited, inertial sensors were attached to L5 and a fall report, following a fall, was used to extract the fall signal. This data-set was examined, and variables were extracted that include upper and lower impact peak values, posture angle change during the fall and time of occurrence. These extracted parameters, can be used to inform the design of fall-detection algorithms for real-world falls detection in the elderly.

Temporal and kinematic variables for real-world falls harvested from lumbar sensors in the elderly population / Bourke, A.K; Klenk, J.; Schwickert, L.; Aminian, K.; Ihlen, E.A.F.; Helbostad, J.L.; Chiari, L.; Becker, C.. - ELETTRONICO. - 2015-:(2015), pp. 7319559.5183-7319559.5186. (Intervento presentato al convegno 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 tenutosi a MiCo Center, Milano Congressi Center, Italy nel 2015) [10.1109/EMBC.2015.7319559].

Temporal and kinematic variables for real-world falls harvested from lumbar sensors in the elderly population

CHIARI, LORENZO;
2015

Abstract

Automatic fall detection will reduce the consequences of falls in the elderly and promote independent living, ensuring people can confidently live safely at home. Inertial sensor technology can distinguish falls from normal activities. However, <7% of studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events. We have extracted temporal and kinematic parameters to further improve the development of fall detection algorithms. A total of 100 real-world falls were analysed. Subjects with a known fall history were recruited, inertial sensors were attached to L5 and a fall report, following a fall, was used to extract the fall signal. This data-set was examined, and variables were extracted that include upper and lower impact peak values, posture angle change during the fall and time of occurrence. These extracted parameters, can be used to inform the design of fall-detection algorithms for real-world falls detection in the elderly.
2015
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
5183
5186
Temporal and kinematic variables for real-world falls harvested from lumbar sensors in the elderly population / Bourke, A.K; Klenk, J.; Schwickert, L.; Aminian, K.; Ihlen, E.A.F.; Helbostad, J.L.; Chiari, L.; Becker, C.. - ELETTRONICO. - 2015-:(2015), pp. 7319559.5183-7319559.5186. (Intervento presentato al convegno 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 tenutosi a MiCo Center, Milano Congressi Center, Italy nel 2015) [10.1109/EMBC.2015.7319559].
Bourke, A.K; Klenk, J.; Schwickert, L.; Aminian, K.; Ihlen, E.A.F.; Helbostad, J.L.; Chiari, L.; Becker, C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/549805
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