Estimating Upper Extremity Fugl-Meyer Assessment Scores From Reaching Motions Using Wearable Sensors.

Abstract

The Fugl Meyer Assessment (FMA) is a widely-used assessment for tracking motor function recovery post-stroke. Due to the limited access to rehabilitation, there exists a need for remote and automated assessment solutions. Wearable sensors and data-driven methods have shown promise for enabling automatic upper extremity FMA (FMA-UE) estimation, but minimizing user input motion and aligning with current clinical activities will aid the adoption of sensor-based assessments. In this work, we present an FMA-UE estimator which can make score predictions for a key subset of the assessment (70$\% $ of all items) using data from inertial measurement units (IMUs) placed on the arms and the trunk from three volitional reaching motions representative of functional daily activities. We collected a dataset of eleven stroke participants performing a subset of FMA-UE, and three reaching motions. The FMA-UE of each participant was assessed by an occupational therapist providing the labeled score for the training data. The estimator was trained on windowed data during FMA-UE motions and was able to make score estimates from reaching motions. Through leave-one-subject-out cross validation, the estimator achieved a normalized RMSE of 7$\% $, which is comparable to or below the established minimal clinically important difference and minimal detectable change of FMA-UE of post-stroke individuals. Comparison experiments of various model designs also revealed the importance of trunk-based features inspired by compensation strategies common post stroke and features extracted from the hand sensor. The proposed estimator has the potential to broaden the possibility of automatic assessment via wearable sensors.