Abstract
Lateral fall is a major cause of hip fractures in elderly people. An automatic fall detection algorithm can reduce the time to get medical help. In this paper, we propose a fall detection algorithm that detects lateral falls by identifying the events in the Linear Prediction (LP) residual of the acceleration experienced by the the body during a fall. The acceleration is measured by a triaxial accelerometer. The accelerometer is attached to an elastic band and is worn around the test subject’s waist. The LP residual is filtered using a Savitzky-Golay filter and the maximum peaks are identified as falls. The results indicate that the lateral falls can be detected using our algorithm with a sensitivity of 84% when falling from standing and 90% when falling from walking.
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Beevi, F.H.A., Pedersen, C.F., Wagner, S., Hallerstede, S. (2014). Lateral Fall Detection via Events in Linear Prediction Residual of Acceleration. In: Ramos, C., Novais, P., Nihan, C., Corchado RodrÃguez, J. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent Systems and Computing, vol 291. Springer, Cham. https://doi.org/10.1007/978-3-319-07596-9_22
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DOI: https://doi.org/10.1007/978-3-319-07596-9_22
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07595-2
Online ISBN: 978-3-319-07596-9
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