title: Mobility profile and wheelchair driving skills of powered wheelchair users: sensor-based event recognition using a support vector machine classifier.
abstract: This paper presents a method to automatically recognize events and driving activities during the use of a powered wheelchair (PW). The method uses a support vector machine classifier, trained from sensor-based data from a datalogging platform installed on the PW. Data from a 3D accelerometer positioned on the back of the PW were collected in a laboratory space during PW driving tasks. 16-segmented events and driving activities (i.e. impacts from different side on different objects, rolling down or up on incline surface, going across threshold of different height) were performed repeatedly (n=25 trials) by one operator at three different speeds (slow, normal, high). We present results from an experiment aiming to classify five different events and driving activities from the sensor data acquired using the datalogging platform. Classification results show the ability of the proposed method to reliably segment 100% of events, and to identify the correct event type in 80% of events.
authors.affiliation: School of Computer Science, McGill University, Montreal, QC, Canada. akarde@cs.mcgill.ca
authors.forename: Athena K
authors.initials: AK
authors.lastname: Moghaddam
authors.forename: Joelle
authors.initials: J
authors.lastname: Pineau
authors.forename: Jordan
authors.initials: J
authors.lastname: Frank
authors.forename: Philippe
authors.initials: P
authors.lastname: Archambault
authors.forename: François
authors.initials: F
authors.lastname: Routhier
authors.forename: Thérèse
authors.initials: T
authors.lastname: Audet
authors.forename: Jan
authors.initials: J
authors.lastname: Polgar
authors.forename: François
authors.initials: F
authors.lastname: Michaud
authors.forename: Patrick
authors.initials: P
authors.lastname: Boissy
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D000375:Aging; D000465:Algorithms; D003201:Computers; D004867:Equipment Design; D006801:Humans; D008328:Man-Machine Systems; D015203:Reproducibility of Results; D012107:Research Design; D012371:Robotics; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D013997:Time Factors; D014584:User-Computer Interface; D014910:Wheelchairs