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

• Published in: Digital biomarkers Vol. 5; no. 2; pp. 158 - 166
• Main Authors: Balestra, Noah, Sharma, Gaurav, Riek, Linda M., Busza, Ania
• Format: Journal Article
• Language: English
• Published: Basel, Switzerland S. Karger AG 01.05.2021; Karger Publishers
• Subjects: Accelerometers; Accuracy; Algorithms; Artificial intelligence; Classification; Data processing; Datasets; Exercise; Extremities; Machine learning; Medical research; Patients; Rehabilitation; rehabilitation research; Research Reports; Research Reports - Research Article; Sensors; Software; Stroke; stroke rehabilitation; supervised machine learning; task performance and analysis; wearable devices; United States > US; Supervised machine learning; Stroke rehabilitation; Task performance and analysis; Wearable devices; Rehabilitation research
• ISSN: 2504-110X; 2504-110X
• DOI: 10.1159/000516619

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.