title: On-line classification of human activity and estimation of walk-run speed from acceleration data using support vector machines.
abstract: The awareness of the physical activity that human subjects perform, and the quantification of activity strength and duration are important tasks that a wearable sensor system would fulfill to be valuable in several biomedical applications, from health monitoring to physical medicine and rehabilitation. In this work we develop a wearable sensor system that collect data from a single thigh-mounted tri-axial accelerometer; the system performs activity classification (sit, stand, cycle, walk, run), and speed estimation for walk (run) labeled data features. These classification/estimation tasks are achieved by cascading two Support Vector Machines (SVM) classifiers. Activity classification accuracy higher than 99% and root mean square errors E(RMS) = 0.28 km/h for speed estimation are obtained in our preliminary experiments. The developed wearable sensor system provides activity labels and speed point estimates at the pace of two readings per second.
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy. a.mannini@sssup.it
authors.forename: Andrea
authors.initials: A
authors.lastname: Mannini
authors.forename: Angelo Maria
authors.initials: AM
authors.lastname: Sabatini
mesh_terms: D000054:Acceleration; D006801:Humans; D012420:Running; D060388:Support Vector Machine; D016138:Walking