Fig 1.
A) Wrist’s DoFs and movements involved in the task (flexion/extension, abduction/adduction and pronation/supination), with reference to an initial neutral position. Wrist motion is measured/actuated by the robotic exoskeleton shown in the figure. B) The temporal sequence of the experimental paradigm. An auditory cue marks the beginning of the trial and the wrist is passively moved by the robotic device from the neutral configuration to the proprioceptive target. After a consistent holding time of 3 seconds, the joint is passively returned to the initial starting position. Another auditory cue indicates participants to start moving and actively reproduce, the joint configuration previously experienced. In this phase the robot is inactive. When the end effector speed is below a 2°/second for more than 2 seconds, the robot moves the wrist back to the neutral position and another trial can start.
Fig 2.
Matching error and variability for flexion/extension (A), abduction/adduction (B) and pronation/supination (C).
The AA results the most accurate DoF (smallest matching error) and the most precise (lowest variability), compared with the other two (black dots). The three ellipses indicate the region that contains 75% of all samples (grey dots), obtained by averaging the 12 active trials for each subject (in total 30 grey dots represented in the figure for each Dof). The ellipses are obtained from the covariance matrix of the sampled data, where the eigenvectors of the covariance matrix represent the axes of the confidence ellipse, and thus models how the data was rotated. The eigenvalues on the other hand represent the variance of the data in the direction of these axes.
Fig 3.
Probability density distributions for the error bias of all the three DoFs in the large (A) and the small (B) workspace.
A distribution shifted to the left indicates subjects’ tendency of undershooting, vice versa, a distribution shifted to the right, representing predominance of positive error biases, indicates a predominant tendency of target overshooting.
Fig 4.
Overall difference, of the three DoFs, between performance in the large (LWS) and in the small (SWS) workspace for matching error (A), variability (B) and error bias (C).
Fig 5.
Differences between performance in large workspace (LSW) and small workspace (SWS) for flexion/extension (A), abduction/adduction (B) and pronation/supination.
Errors which fall below the 45° line (equality line) indicate a worst performance in the LWS, whereas values which fall above the equality line indicate decrement in accuracy/precision in the SWS condition. Data points that fall directly on the equality line indicate that performance is equal in the two workspaces.