Research Article
Issue Date: June 14, 2019
Published Online: June 14, 2019
Updated: June 15, 2019
Capturing Upper Limb Gross Motor Categories Using the Kinect® Sensor
Author Affiliations
  • Na Jin Seo, PhD, is Associate Professor, Division of Occupational Therapy, Department of Health Professions, and Associate Professor, Department of Health Science and Research, Medical University of South Carolina, Charleston; seon@musc.edu
  • Vincent Crocher, PhD, is Research Associate, School of Engineering, University of Melbourne, Parkville, Victoria, Australia. At the time of the study, he was Postdoctoral Researcher, Department of Industrial and Manufacturing Engineering, University of Wisconsin–Milwaukee.
  • Egli Spaho, DPT, is Physical Therapist, Ascension All Saints Hospital, Racine, Wisconsin. At the time of the study, he was Research Assistant, Department of Kinesiology, University of Wisconsin–Milwaukee.
  • Charles R. Ewert, BS, is Associate Software Engineer, Northwestern Mutual, Milwaukee, Wisconsin. At the time of the study, he was Research Assistant, Department of Computer Science, University of Wisconsin–Milwaukee.
  • Mojtaba F. Fathi, PhD, is Research Associate, Department of Mechanical Engineering, University of Wisconsin–Milwaukee.
  • Pilwon Hur, PhD, is Assistant Professor, Department of Mechanical Engineering, Texas A&M University, College Station.
  • Sara A. Lum, MS, OTR/L, is Occupational Therapist, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina. At the time of the study, she was Student, Division of Occupational Therapy, Department of Health Professions, Medical University of South Carolina, Charleston.
  • Elizabeth M. Humanitzki, MS, OTR/L, is Occupational Therapist, Coastal Therapy Services Inc., Charleston, South Carolina. At the time of the study, she was Student, Division of Occupational Therapy, Department of Health Professions, Medical University of South Carolina, Charleston.
  • Abigail L. Kelly, MS, is Instructor, Department of Stomatology, Medical University of South Carolina, Charleston. At the time of the study, she was Research Associate, Department of Public Health Sciences, Medical University of South Carolina, Charleston.
  • Viswanathan Ramakrishnan, PhD, is Professor, Department of Public Health Sciences, Medical University of South Carolina, Charleston.
  • Michelle L. Woodbury, PhD, OTR/L, is Associate Professor, Division of Occupational Therapy, Department of Health Professions, and Associate Professor, Department of Health Science and Research, Medical University of South Carolina, Charleston.
Article Information
Hand and Upper Extremity / Neurologic Conditions / Rehabilitation, Participation, and Disability / Stroke / Research Articles
Research Article   |   June 14, 2019
Capturing Upper Limb Gross Motor Categories Using the Kinect® Sensor
American Journal of Occupational Therapy, 06 2019, Vol. 73, 7304205090. https://doi.org/10.5014/ajot.2019.031682
American Journal of Occupational Therapy, 06 2019, Vol. 73, 7304205090. https://doi.org/10.5014/ajot.2019.031682
Abstract

Importance: Along with growth in telerehabilitation, a concurrent need has arisen for standardized methods of tele-evaluation.

Objective: To examine the feasibility of using the Kinect sensor in an objective, computerized clinical assessment of upper limb motor categories.

Design: We developed a computerized Mallet classification using the Kinect sensor. Accuracy of computer scoring was assessed on the basis of reference scores determined collaboratively by multiple evaluators from reviewing video recording of movements. In addition, using the reference score, we assessed the accuracy of the typical clinical procedure in which scores were determined immediately on the basis of visual observation. The accuracy of the computer scores was compared with that of the typical clinical procedure.

Setting: Research laboratory.

Participants: Seven patients with stroke and 10 healthy adult participants. Healthy participants intentionally achieved predetermined scores.

Outcomes and Measures: Accuracy of the computer scores in comparison with accuracy of the typical clinical procedure (immediate visual assessment).

Results: The computerized assessment placed participants’ upper limb movements in motor categories as accurately as did typical clinical procedures.

Conclusions and Relevance: Computerized clinical assessment using the Kinect sensor promises to facilitate tele-evaluation and complement telehealth applications.

What This Article Adds: Computerized clinical assessment can enable patients to conduct evaluations remotely in their homes without therapists present.