title: Identifying the number and location of body worn sensors to accurately classify walking, transferring and sedentary activities.
abstract: In order to perform fall risk assessments using wearable inertial sensors in older adults in their natural settings where falls are likely to occur, a first step is to automatically segment and classify sensor signals of human movements into the known `activities of interest'. Sensor data from such activities can later be used through quantitative and qualitative analysis for differentiating fallers from non-fallers. In this study, ten young adults participated in experimental trials involving several variations of walking, transferring and sedentary activities. Data from tri-axial accelerometers and gyroscopes were used to classify the aforementioned three categories using a multiclass support vector machine algorithm. Our results showed 100% accuracy in distinguishing walking, transferring and sedentary activities using data from a three-sensor combination of sternum and both ankles.
authors.forename: Omar
authors.initials: O
authors.lastname: Aziz
authors.forename: Stephen N
authors.initials: SN
authors.lastname: Robinovitch
authors.forename: Edward J
authors.initials: EJ
authors.lastname: Park
mesh_terms: D000054:Acceleration; D000328:Adult; D000465:Algorithms; D005260:Female; D018594:Human Body; D006801:Humans; D008297:Male; D010827:Physiology / Q000295:instrumentation*; D057185:Sedentary Behavior; D012680:Sensitivity and Specificity; D016138:Walking / Q000502:physiology*; D055815:Young Adult