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

Sebastian Bersch, Christian Chislett, Djamel Azzi, Rinat Khusainov, Jim Briggs

Research output: Contribution to conferencePaperpeer-review

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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.
Original languageEnglish
Publication statusPublished - 23 May 2011
EventWorkshop on Cognitive Sensor Networks for Pervasive Health - Dublin, Ireland
Duration: 23 May 2011 → …

Conference

ConferenceWorkshop on Cognitive Sensor Networks for Pervasive Health
Abbreviated titleCoSNPH-2011
Country/TerritoryIreland
CityDublin
Period23/05/11 → …

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