title: A system for activity recognition using multi-sensor fusion.
abstract: This paper proposes a system for activity recognition using multi-sensor fusion. In this system, four sensors are attached to the waist, chest, thigh, and side of the body. In the study we present two solutions for factors that affect the activity recognition accuracy: the calibration drift and the sensor orientation changing. The datasets used to evaluate this system were collected from 8 subjects who were asked to perform 8 scripted normal activities of daily living (ADL), three times each. The Naïve Bayes classifier using multi-sensor fusion is adopted and achieves 70.88%-97.66% recognition accuracies for 1-4 sensors.
authors.affiliation: Department of Electronic and Computer Engineering, Faculty of Science and Engineering, University of Limerick, Ireland. lei.gao@ul.ie
authors.forename: Lei
authors.initials: L
authors.lastname: Gao
authors.forename: Alan K
authors.initials: AK
authors.lastname: Bourke
authors.forename: John
authors.initials: J
authors.lastname: Nelson
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D000369:Aged, 80 and over; D002138:Calibration; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted