Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

• Main Authors: Catherine Adans-Dester, Nicolas Hankov, Anne O’Brien, Gloria Vergara-Diaz, Randie Black-Schaffer, Ross Zafonte, Jennifer Dy, Sunghoon I. Lee, Paolo Bonato
• Format: Article
• Language: English
• Published: Nature Publishing Group 2020-09-01
• Series: npj Digital Medicine
• Online Access: https://doi.org/10.1038/s41746-020-00328-w

Abstract The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for “precision rehabilitation”. Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients’ responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.