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Activity detection using frequency analysis and off-the-shelf devices: Fall detection from accelerometer data

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5 Author(s)
Bersch, S.D. ; Univ. of Portsmouth, Portsmouth, UK ; Chislett, C.M.J. ; Azzi, D. ; Khusainov, R.
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Increasingly, applications of technology are being developed to provide care to elderly and vulnerable people living alone. This paper looks at using sensors to monitor a person's wellbeing. The paper attempts to recognise and distinguish falling, sitting and walking activities from accelerometer data. Fast Fourier Transformation (FFT) is used to extract information from collected data. The low-cost accelerometer is part of a Texas Instruments watch. Our experiments focus on lower sampling rates than those used elsewhere in the literature. We show that a sampling rate of 10Hz from a wrist-worn device does not reliably distinguish between a fall and merely sitting down.

Published in:

Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2011 5th International Conference on

Date of Conference:

23-26 May 2011