Human postures recognition based on D-S evidence theory and multi-sensor data fusion

W. Li, J. Bao, X. Fu, G. Fortino, S. Galzarano

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

19 Citations (Scopus)
2 Downloads (Pure)

Abstract

Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).
Original languageEnglish
Title of host publicationProceedings of the 12th IEEE International Symposium on Cluster Computing and the Grid Computing, 13-16 May 2012, Ottawa, Canada
Place of PublicationPiscataway
PublisherIEEE Computer Society
Pages912-917
ISBN (Print)978-0-7695-4691-9
DOIs
Publication statusPublished - 2012

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