Using Wearable Sensors and Machine Learning Models to Separate Functional Upper Extremity Use From Walking-Associated Arm Movements.

Journal: Archives of physical medicine and rehabilitation
Published Date: Oct 3, 2015

Abstract

OBJECTIVE: To improve measurement of upper extremity (UE) use in the community by evaluating the feasibility of using body-worn sensor data and machine learning models to distinguish productive prehensile and bimanual UE activity use from extraneous movements associated with walking.

Authors

  • Adam McLeod
    MITRE Corporation, McLean, VA.
  • Elaine M Bochniewicz
    MITRE Corporation, McLean, VA; Department of Biomedical Engineering, Catholic University of America, Washington, DC.
  • Peter S Lum
  • Rahsaan J Holley
  • Geoff Emmer
    MITRE Corporation, McLean, VA.
  • Alexander W Dromerick
    MedStar National Rehabilitation Network, Washington, DC; Washington DC Veterans Affairs Medical Center, Washington, DC; Center for Brain Plasticity and Recovery, Georgetown University, Washington, DC; Department of Rehabilitation Medicine, Georgetown University, Washington, DC; Department of Neurology, Georgetown University, Washington, DC. Electronic address: Alexander.w.dromerick@medstar.net.

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