Abstract
In this article we present a human motion detection framework, based on data derived from a single tri-axial accelerometer. The framework uses a set of different preprocessing methods that produce data representations which are respectively parameterized by statistical and physical features. These features are then concatenated and classified using well-known classification algorithms for the problem of motion recognition. Experimental evaluation was carried out according to a subject-dependent scenario, meaning that the classification is performed for each subject separately using their own data and the average accuracy for all individuals is computed. The best achieved detection performance for 14 everyday human motion activities, using the USC-HAD database, was approximately 95%. The results compare favorably are competitive to the best reported performance of 93.1% for the same database.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
