Research Article
Activity detection using frequency analysis and off-the-shelf devices: Fall detection from accelerometer data
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2011.246119, author={Sebastian Bersch and Christian Chislett and Djamel Azzi and Rinat Khusainov and Jim Briggs}, title={Activity detection using frequency analysis and off-the-shelf devices: Fall detection from accelerometer data}, proceedings={Cognitive Sensor Networks for Pervasive Health}, publisher={IEEE}, proceedings_a={COSN-PH}, year={2012}, month={4}, keywords={Activity Detection Fall Recognition Accelerometer Remote Healthcare Delivery}, doi={10.4108/icst.pervasivehealth.2011.246119} }- Sebastian Bersch
Christian Chislett
Djamel Azzi
Rinat Khusainov
Jim Briggs
Year: 2012
Activity detection using frequency analysis and off-the-shelf devices: Fall detection from accelerometer data
COSN-PH
IEEE
DOI: 10.4108/icst.pervasivehealth.2011.246119
Abstract
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.
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