Movement distributions of stroke survivors exhibit distinct patterns that evolve with training

Abstract

Background: While clinical assessments provide tools for characterizing abilities in motor-impaired individuals, concerns remain over their repeatability and reliability. Typical robot-assisted training studies focus on repetition of prescribed actions, yet such movement data provides an incomplete account of abnormal patterns of coordination. Recent studies have shown positive effects from self-directed movement, yet such a training paradigm leads to challenges in how to quantify and interpret performance. Methods: With data from chronic stroke survivors (n = 10, practicing for 3 days), we tabulated histograms of the displacement, velocity, and acceleration for planar motion, and examined whether modeling of distributions could reveal changes in available movement patterns. We contrasted these results with scalar measures of the range of motion. We performed linear discriminant analysis (LDA) classification with selected histogram features to compare predictions versus actual subject identifiers. As a basis of comparison, we also present an age-matched control group of healthy individuals (n = 10, practicing for 1 day). Results: Analysis of range of motion did not show improvement from self-directed movement training for the stroke survivors in this study. However, examination of distributions indicated that increased multivariate normal components were needed to accurately model the patterns of movement after training. Stroke survivors generally exhibited more complex distributions of motor exploration compared to the age-matched control group. Classification using linear discriminant analysis revealed that movement patterns were identifiable by individual. Individuals in the control group were more difficult to identify using classification methods, consistent with the idea that motor deficits contribute significantly to unique movement signatures. Conclusions: Distribution analysis revealed individual patterns of abnormal coordination in stroke survivors and changes in these patterns with training. These findings were not apparent from scalar metrics that simply summarized properties of motor exploration. Our results suggest new methods for characterizing motor capabilities, and could provide the basis for powerful tools for designing customized therapy.

Personalized robot-assisted dressing using user modeling in latent spaces

“…To provide personalized assistance, one typical approach is human movement modeling. Multivariate Gaussian distributions has been successfully used to model the planar hand motion for robotic rehabilitation [10,11]. To model the upper-body movements in 3D coordinates, Gaussian Mixture Models (GMMs) have been used in our previous research [12] to model the movement space of each upper-body joint of the user (hand, elbow and shoulder) separately.…”

Mapping upper-limb motor performance after stroke - a novel method with utility for individualized motor training

“…High performance variability due to loose constraints on the movement trajectory may be another contributor to the limited sensitivity of their approach. Recently, a preliminary --but more sophisticated - approach for mapping performance distribution has been suggested [34], though is not yet utilizable as a tool for assessment of motor impairment.…”

Probabilistic Real-Time User Posture Tracking for Personalized Robot-Assisted Dressing

“…have used this information to update the robot trajectory accordingly. Other studies [51][52][53][54] have described the user movement capabilities to personalize robot assistance rather than user preferences. Multivariate Gaussian distributions have been successfully used to statistically identify the range of hand movement capabilities in a planar space for robotic rehabilitation [51,52].…”

“…Other studies [51][52][53][54] have described the user movement capabilities to personalize robot assistance rather than user preferences. Multivariate Gaussian distributions have been successfully used to statistically identify the range of hand movement capabilities in a planar space for robotic rehabilitation [51,52]. [53] has modeled the movement velocity of stroke survivors' arms with a multivariate Gaussian kernel, and accordingly personalized a robot-applied force intervention that encourages under-expressed movements.…”