A body-worn sensor system utilizing both accelerometry and gyroscopy data with a machine learning algorithm achieved an overall exercise repetition counting accuracy of 95.6% compared to manual counting.
Observational (n=26)
No
Can body-worn sensors and machine learning accurately identify and count upper extremity rehabilitation exercise repetitions in patients with recent stroke and healthy controls?
Body-worn sensor systems combined with machine learning can accurately identify and count upper extremity rehabilitation exercises in post-stroke patients, demonstrating feasibility for tracking exercise dose.
Abstract Background: Prior studies suggest that participation in rehabilitation exercises improves motor function post-stroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. Objective: In this exploratory study, we assessed the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigated which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. Methods: MC10 BioStampRC® sensors were used to measure accelerometry and gyroscopy data from arms of healthy controls (n=13) and patients with upper extremity (UE) weakness due to recent stroke (n=13) while the subjects performed three pre-selected UE exercises. Sensor data was 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 ± 2.4 % overall, and 95.0 ± 2.3 % in patients with UE weakness due to stroke. Accuracy was decreased when using fewer sensors or using accelerometry 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 post-stroke patients during clinical rehabilitation or clinical trials.
Balestra et al. (Thu,) conducted a observational in Upper extremity weakness due to recent stroke (n=26). Body-worn sensors (accelerometry and gyroscopy) with machine learning vs. Manual repetition counting was evaluated on Repetition counting accuracy. A body-worn sensor system utilizing both accelerometry and gyroscopy data with a machine learning algorithm achieved an overall exercise repetition counting accuracy of 95.6% compared to manual counting.