Background: Human-administered clinical scales are the accepted standard for quantifying motor performance of stroke subjects. Although they are widely accepted, these measurement tools are limited by interrater and intrarater reliability and are time-consuming to apply. In contrast, robot-based measures are highly repeatable, have high resolution, and could potentially reduce assessment time. Although robotic and other objective metrics have proliferated in the literature, they are not as well established as clinical scales and their relationship to clinical scales is mostly unknown.
Objective: To test the performance of linear regression models to estimate clinical scores for the upper extremity from systematic robot-based metrics.
Methods: Twenty kinematic and kinetic metrics were derived from movement data recorded with the shoulder-and-elbow InMotion2 robot (Interactive Motion Technologies, Inc), a commercial version of the MIT-Manus. Kinematic metrics were aggregated into macro-metrics and micro-metrics and collected from 111 chronic stroke subjects. Multiple linear regression models were developed to calculate Fugl-Meyer Assessment, Motor Status Score, Motor Power, and Modified Ashworth Scale from these robot-based metrics.
Results: Best performance-complexity trade-off was achieved by the Motor Status Score model with 8 kinematic macro-metrics (R = .71 for training; R = .72 for validation). Models including kinematic micro-metrics did not achieve significantly higher performance. Performances of the Modified Ashworth Scale models were consistently low (R = .35-.42 for training; R = .08-.17 for validation).
Conclusions: The authors identified a set of kinetic and kinematic macro-metrics that may be used for fast outcome evaluations. These metrics represent a first step toward the development of unified, automated measures of therapy outcome.