Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch.

Journal: Physiological measurement

Published Date: Jul 23, 2018

Abstract

OBJECTIVE: Participation in a physical therapy program is considered one of the greatest predictors of successful conservative management of common shoulder disorders. However, adherence to these protocols is often poor and typically worse for unsupervised home exercise programs. Currently, there are limited tools available for objective measurement of adherence in the home setting. The goal of this study was to develop and evaluate the potential for performing home shoulder physiotherapy monitoring using a commercial smartwatch.

Authors

David M Burns

Division of Orthopaedic Surgery, University of Toronto, Toronto, Canada.
Nathan Leung
Michael Hardisty
Cari M Whyne
Patrick Henry

Cardiology Department, Lariboisière Hospital, Assistance Publique Hôpitaux de Paris, Université Paris VII Denis Diderot, Paris, France.
Stewart McLachlin

Keywords

Adult Computers, Handheld Exercise Therapy Female Healthy Volunteers Humans Machine Learning Male Middle Aged Monitoring, Physiologic Neural Networks, Computer Patient Compliance Physical Therapy Modalities Shoulder Signal Processing, Computer-Assisted Support Vector Machine Young Adult

External Resources

View on PubMed Access via DOI PubMed (29952759)

Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch.

Abstract

Authors

Keywords

External Resources

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