Correlations between statistical models of robotically collected kinematics and clinical measures of upper extremity function

Abstract

One of the obstacles in the development of rehabilitation robotics has been inadequacy in the measurement of treatment effects due to interventions. A measurement tool that will efficiently produce a large reliable sample of measurements collected during a single session that can also produce a rich set of data which reflects a subject's ability to perform meaningful functional activities has not been developed. This paper presents three linear regression models generated from seven kinematic measures collected during the performance of virtually simulated rehabilitation activities that were integrated with haptic robots by 19 persons with upper extremity hemiparesis due to chronic stroke. One of these models demonstrated a statistically significant correlation with the subjects' scores on the Jebsen Test of Hand Function (JTHF), a battery of six standardized upper extremity functional activities. The second and third models demonstrated a statistically significant correlation with the subjects' change scores on the JTHF.

Figures

Figure 1

Scatter plot of actual scores on JTHF in seconds on x-axis and model-predicted JTHF score on y-axis. Each subject is represented by two points, one for pre-test data and a second for post-test data. Lower values indicate better score.

Figure 2

Scatter plot of change scores for JTHF in seconds (pre test score minus post test score) on x-axis and model predicted scores on y axis. Model was generated from change in kinematics (pre test scores minus post test scores) and actual JTHF scores

Figure 3

Scatter plot of change scores for JTHF in seconds (pre test score minus post test score) on x-axis and model predicte scores on y-axis. Model was generated from pre test kinematics scores.