Muscle forces comprise a more sensitive measure of post-stroke movement deficits than joint angles

Background and PurposeThe whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. The effect of damage to the nervous system such as stroke on the complex multi-joint motion is difficult to quantify in a meaningful way that informs about the underlying deficit in the neural control of movement. We tested the idea that the disruption in intersegmental coordination after stroke can be quantified with higher sensitivity using metrics based on forces rather than motion. Our study aim was to objectively quantify post-stroke motor deficits using motion capture of stereotypical reaching movements. Our hypothesis is that muscle forces estimated based on active joint torques are a more sensitive measure of post-stroke motor deficits than angular kinematics.MethodsThe motion of twenty-two participants was captured when reaching to virtual targets in a center-out task. We used inverse dynamic analysis to derive muscle torques, which were the result of the neural control signals to muscles to produce the recorded multi-joint movements. We then applied a novel analysis to separate the component of muscle torque related to gravity compensation from that related to motion production. We used the kinematic and dynamic variables derived from motion capture to assess age-related and post-stroke motor deficits.ResultsWe found that reaching with the non-dominant arm was accomplished with shoulder and elbow torques that had larger amplitudes and inter-trial variability compared to reaching with the dominant arm. These dominance effects confounded the assessment of post-stroke motor deficits using amplitude and variability metrics. We then identified the metric based on waveform comparison that was insensitive to dominance effects. We used it to show that muscle torques with gravity-related components subtracted were much more sensitive to post-stroke motor deficits compared to measures based on joint angles. Using this metric, it was possible to quantify the extent of individual deficits caused by stroke independently from age-related deficits and dominance effects.ConclusionsFunctional deficits seen in task performance have biomechanical underpinnings, seen only through force-based analysis. Our study has shown that estimating muscle forces that drive motion can quantify with high sensitivity post-stroke deficits in intersegmental coordination. A force-based assessment developed based on this method could help quantify less “observable” deficits in mildly affected stroke patients, such as those classified as asymptomatic via traditional motion-based assessments, but who may still report difficulty moving, increased fatigue, and/or inactivity. Moreover, identifying deficits in the different components of muscle forces may be a way to personalize and standardize intervention and increase the effectiveness of robotic therapy.