title: Detection of physical activities using a physical activity monitor system for wheelchair users.
abstract: Availability of physical activity monitors for wheelchair users can potentially assist these individuals to track regular physical activity (PA), which in turn could lead to a healthier and more active lifestyle. Therefore, the aim of this study was to develop and validate algorithms for a physical activity monitoring system (PAMS) to detect wheelchair based activities. The PAMS consists of a gyroscope based wheel rotation monitor (G-WRM) and an accelerometer device (wocket) worn on the upper arm or on the wrist. A total of 45 persons with spinal cord injury took part in the study, which was performed in a structured university-based laboratory environment, a semi-structured environment at the National Veterans Wheelchair Games, and in the participants' home environments. Participants performed at least ten PAs, other than resting, taken from a list of PAs. The classification performance for the best classifiers on the testing dataset for PAMS-Arm (G-WRM and wocket on upper arm) and PAMS-Wrist (G-WRM and wocket on wrist) was 89.26% and 88.47%, respectively. The outcomes of this study indicate that multi-modal information from the PAMS can help detect various types of wheelchair-based activities in structured laboratory, semi-structured organizational, and unstructured home environments.
authors.affiliation: Department of Veterans Affairs, Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, PA, United States; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States.
authors.forename: Shivayogi V
authors.initials: SV
authors.lastname: Hiremath
authors.affiliation: College of Computer and Information Science, Northeastern University, Boston, MA, United States; Department of Health Sciences, Northeastern University, Boston, MA, United States.
authors.forename: Stephen S
authors.initials: SS
authors.lastname: Intille
authors.affiliation: Department of Veterans Affairs, Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA, United States.
authors.forename: Annmarie
authors.initials: A
authors.lastname: Kelleher
authors.affiliation: Department of Veterans Affairs, Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA, United States; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.
authors.forename: Rory A
authors.initials: RA
authors.lastname: Cooper
authors.affiliation: Department of Veterans Affairs, Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, PA, United States; Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA, United States; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States. Electronic address: dad5@pitt.edu.
authors.forename: Dan
authors.initials: D
authors.lastname: Ding
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001132:Arm / Q000502:physiology; D001185:Artificial Intelligence; D040421:Cell Phone; D004777:Environment; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity / Q000502:physiology; D010264:Paraplegia / Q000209:etiology / Q000503:physiopathology; D013119:Spinal Cord Injuries / Q000150:complications / Q000503:physiopathology; D014910:Wheelchairs; D055815:Young Adult
keywords: Activity monitor system; Classification; Machine learning; Physical activity; Smartphones; Spinal cord injury; Wheelchair users