Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting.

Journal: Journal of medical Internet research
Published Date: May 25, 2017

Abstract

BACKGROUND: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals collected in a laboratory setting. However, movement patterns change after a stroke (eg, gait impairment), and activities may be performed differently at home than in a lab. Thus, it is important to validate AR for gait-impaired stroke patients in a home setting for accurate clinical predictions.

Authors

  • Megan K O'Brien
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Rehabilitation Institute of Chicago, Chicago, IL, United States.
  • Nicholas Shawen
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611 USA.
  • Chaithanya K Mummidisetty
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Rehabilitation Institute of Chicago, 345 E. Superior St, Chicago, IL, 60611, USA. k-mummidisetty@ricres.org.
  • Saninder Kaur
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Rehabilitation Institute of Chicago, Chicago, IL, United States.
  • Xiao Bo
    Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
  • Christian Poellabauer
    Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
  • Konrad Kording
    Laura Prosser, PhD, PTR is a Assistant Professor of Pediatrics, the Perelman School of Medicine, University of Pennsylvania and a physical therapist, Children's Hospital of Philadelphia.
  • Arun Jayaraman
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611 USA.

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