In-lab versus at-home activity recognition in ambulatory subjects with incomplete spinal cord injury.

Journal: Journal of neuroengineering and rehabilitation
Published Date: Feb 6, 2017

Abstract

BACKGROUND: Although commercially available activity trackers can aid in tracking therapy and recovery of patients, most devices perform poorly for patients with irregular movement patterns. Standard machine learning techniques can be applied on recorded accelerometer signals in order to classify the activities of ambulatory subjects with incomplete spinal cord injury in a way that is specific to this population and the location of the recording-at home or in the clinic.

Authors

  • Mark V Albert
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States.
  • Yohannes Azeze
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Rehabilitation Institute of Chicago, Chicago, USA.
  • Michael Courtois
    Department of Physical Medicine and Rehabilitation, Northwestern University, Evanston, USA.
  • Arun Jayaraman
    Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611 USA.

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