End-point kinematics using virtual reality explaining upper limb impairment and activity capacity in stroke

Abstract

Background For evaluation of upper limb impairment and activity capacity, Fugl-Meyer Assessment of Upper Extremity (FMA-UE) and Action Research Arm Test (ARAT) are recommended to be included in stroke trials. To improve the understanding of mechanisms of motor recovery, and differentiate between restitution and compensation, kinematic analysis is also recommended for assessment of upper limb function after stroke. Aim To determine the extent to which end-point kinematic variables obtained from the target-to-target pointing task were associated with upper limb impairment or activity limitation as assessed by traditional clinical scales in individuals with stroke. Methods Sixty-four individuals, from acute stage up to one year after stroke, were included from the Stroke Arm Longitudinal study at the University of Gothenburg (SALGOT) cohort. They performed a target-to-target pointing task in a virtual environment using a haptic stylus which also captured the kinematic parameters. Multiple linear regression was done to determine the amount of variance explained by kinematic variables on FMA-UE and ARAT scores after controlling for confounding variables. Results Mean velocity and number of velocity peaks explained 11 and 9% of the FMA-UE score uniquely and 16% when taken together. Movement time and number of velocity peaks explained 13 and 10% of the ARAT score respectively. Conclusion The kinematic variables of movement time, velocity and smoothness explain only a part of the variance captured by using clinical observational scales, reinforcing the importance of multi-level assessment using both kinematic analysis and clinical scales in upper limb evaluation after stroke. Trial registration The trial was registered with register number NCT01115348 at clinicaltrials.gov , on May 4, 2010. URL: https://clinicaltrials.gov/ct2/show/NCT01115348 .

Relationship between self-reported and objectively measured manual ability varies during the first year post-stroke

“…One possible reason for the observed stronger correlations of movement time and number of velocity peaks with ABILHAND logits at later time points might be that these deficits are more easily noticed in real life activities than others. Movement time and number of movement units are also strongly correlated (0.87) which might as well explain the similar pattern seen in these variables 21 . The relationship between ABILHAND logits and kinematic variables at later time points might have been influenced by the decreased variability when more participants reached near-maximal scores of the scales.…”

Design a Haptic-Combined Virtual Reality System to Improve Box and Block Test (BBT) for Upper Extremity Function Assessment

“…However, FMA-UE scores showed weak and moderate correlations with the VBBT measurements ( Table 2). The ARAT, related to the International Classi cation of Functioning, Disability and Health (ICF) Activity level [18], is usually used for concurrent validity of UE function assessment with novel technology [35,39]. The FMA-UE has also been commonly used for concurrent validity.…”

“…1c). Considering previously validated parameters for UE assessment in the literature [28,34,35], the kinematic parameters used in the VBBT were determined and included the following: 1) N ZC -ACC: The number of zero-crossings of the moving acceleration in a block transfer, which was used to assess the smoothness and coordination of UE movement.…”

Comparison of the effects of and usability of active and active-assistive rehabilitation robots for the upper extremity function among patients with stroke: a single-blinded randomized controlled pilot study

“…Fourth, the kinematic analysis had detected distinct feature that were not explained by clinical scale scores. [21] Notably, the ACT rehabilitation robot showed more bene cial effects with respect to SIS-function and SIS-social participation than did the ACAS rehabilitation robot. Active assistance could induce "motor slacking" of participants, which is a tendency to minimize metabolic and movement-related costs, thereby preventing active participation and simultaneously developing dependence on the robot.…”