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Sensors 2013, 13(7), 9183-9200; doi:10.3390/s130709183
Article

Optimal Placement of Accelerometers for the Detection of Everyday Activities

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1 School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim, Northern Ireland BT37 0QB, UK 2 Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå 971 87, Sweden 3 Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry, Northern Ireland BT52 1SA, UK
* Author to whom correspondence should be addressed.
Received: 27 April 2013 / Revised: 28 June 2013 / Accepted: 9 July 2013 / Published: 17 July 2013
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in the UK 2013)
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Abstract

This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.
Keywords: activity recognition; accelerometery; wearable technology; classification models activity recognition; accelerometery; wearable technology; classification models
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Cleland, I.; Kikhia, B.; Nugent, C.; Boytsov, A.; Hallberg, J.; Synnes, K.; McClean, S.; Finlay, D. Optimal Placement of Accelerometers for the Detection of Everyday Activities. Sensors 2013, 13, 9183-9200.

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