Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

Noah Balestra; Gaurav Sharma; Linda M Riek; Ania Busza

doi:10.1159/000516619

Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

Digit Biomark. 2021 Jul 2;5(2):158-166. doi: 10.1159/000516619. eCollection 2021 May-Aug.

Authors

Noah Balestra¹, Gaurav Sharma^{2

3

4}, Linda M Riek⁵, Ania Busza¹

Affiliations

¹ Department of Neurology, University of Rochester, Rochester, New York, USA.
² Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA.
³ Department of Computer Science, University of Rochester, Rochester, New York, USA.
⁴ Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.
⁵ Department of Physical Therapy, Nazareth College, Rochester, New York, USA.

Abstract

Background: Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days.

Objectives: The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions.

Methods: MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (n = 13) and individuals with upper extremity weakness due to recent stroke (n = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets.

Results: We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone.

Conclusions: Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise "dose" in poststroke patients during clinical rehabilitation or clinical trials.

Keywords: Rehabilitation research; Stroke rehabilitation; Supervised machine learning; Task performance and analysis; Wearable devices.