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Optimal Placement of Accelerometers for the Detection of Everyday Activities
School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim, Northern Ireland BT37 0QB, UK.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8922-012X
School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim, Northern Ireland BT37 0QB, UK.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
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2013 (English)In: Sensors, E-ISSN 1424-8220, Vol. 13, no 7, p. 9183-9200Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. Vol. 13, no 7, p. 9183-9200
Keywords [en]
activity recognition, accelerometery, wearable technology, classification models
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-5827DOI: 10.3390/s130709183ISI: 000328612800062PubMedID: 23867744Scopus ID: 2-s2.0-84891471224Local ID: 403d5d70-017f-4e22-8430-1117523ed033OAI: oai:DiVA.org:ltu-5827DiVA, id: diva2:978703
Note

Validerad; 2014; 20140120 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2023-09-09Bibliographically approved

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Kikhia, BaselBoytsov, AndreyHallberg, JosefSynnes, Kåre

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