Na Jin Seo, Vincent Crocher, Egli Spaho, Charles R. Ewert, Mojtaba F. Fathi, Pilwon Hur, Sara A. Lum, Elizabeth M. Humanitzki, Abigail L. Kelly, Viswanathan Ramakrishnan, Michelle L. Woodbury; Capturing Upper Limb Gross Motor Categories Using the Kinect® Sensor. Am J Occup Ther 2019;73(4):7304205090p1-7304205090p10. doi: 10.5014/ajot.2019.031682.
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© 2019 American Occupational Therapy Association
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.
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