MiGo: wearable sensors that combine actionable data with a behavioral intervention to improve function after stroke

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One of the most impenetrable problems challenging full recovery after stroke is the gap between motor capacity that a stroke survivor regains (i.e. what they can do) and how they engage in home and community activities (i.e. what they choose to do). To address this challenge, Flint Rehab developed MiGo, a novel multi-sensor activity tracker specifically designed for stroke survivors. The unique feature of MiGo is the ability to capture and deliver feedback on both quantitative and qualitative upper and lower limb activity of stroke survivors in their natural environment using the same system. The long-term goal for this project and the MiGo technology is to develop a data-driven and clinically informed behavioral intervention strategy that uses actionable quantitative and qualitative feedback to maximize physical function after stroke. This project combines Flint Rehab’s technology for real-time motion capture and expertise in developing neurorehabilitation devices with the extensive experience in stroke neurorehabilitation of the Motor Behavior and Neurorehabilitation team at the University of Southern California. This Phase I STTR aims to establish the feasibility, validity, accuracy and usability of MiGo to monitor functional movement behaviors and deliver meaningful feedback to stroke survivors across a broad range of motor impairments seen in this population (Aim 1). Specifically, 30 individuals expressing a range (i.e., mild-severe) of motor impairment chronically after stroke will be recruited to participate in this project. MiGo’s accuracy and utility cost to the end user will be assessed in a single in-lab session. Participants will be outfitted with MiGo and perform functional standardized assessments. As gold-standard comparison, upper limb movements will be compared to movement counts derived from a video recording of the standardized assessments, whereas step counts and stance/stride time will be derived from the ADPM sensors during a walk test. Raw sensor data from MiGo will be analyzed using proprietary algorithms for movement detection and compared to the gold-standards to determine accuracy. Utility cost will be assessed using quantitative survey (social acceptability and ease-of-use), sensor cost and time to don and doff each sensor to determine the minimal number of MiGo sensors and the optimal placement on the body. In a subsequent step (Aim 2), short term feasibility and usability of the optimized MiGo will be established by monitoring community-dwelling stroke survivors using MiGo over a 1-week interval in the natural environment. Adherence, occurrence of adverse events, and satisfaction with MiGo will be recorded. Using the data from the monitoring period, participants will be presented with a ‘Movement report’ with visual displays of quantitative and qualitative feedback (Aim 3). Through survey and qualitative interview, we will identify the components of MiGo feedback that users find most meaningful for driving lasting behavior change. The resulting technology will integrate engineering and patient-centered rehabilitation approaches to promote fuller participation in meaningful life activities outside clinical settings in a less structured environment—one where stroke survivors live their lives.