Upper Limb Kinematics in Stroke and Healthy Controls Using Target-to-Target Task in Virtual Reality

Abstract

BackgroundKinematic analysis using virtual reality (VR) environment provides quantitative assessment of upper limb movements. This technique has rarely been used in evaluating motor function in stroke despite its availability in stroke rehabilitation.ObjectiveTo determine the discriminative validity of VR-based kinematics during target-to-target pointing task in individuals with mild or moderate arm impairment following stroke and in healthy controls.MethodsSixty-seven participants with moderate (32–57 points) or mild (58–65 points) stroke impairment as assessed with Fugl-Meyer Assessment for Upper Extremity were included from the Stroke Arm Longitudinal study at the University of Gothenburg—SALGOT cohort of non-selected individuals within the first year of stroke. The stroke groups and 43 healthy controls performed the target-to-target pointing task, where 32 circular targets appear one after the other and disappear when pointed at by the haptic handheld stylus in a three-dimensional VR environment. The kinematic parameters captured by the stylus included movement time, velocities, and smoothness of movement.ResultsThe movement time, mean velocity, and peak velocity were discriminative between groups with moderate and mild stroke impairment and healthy controls. The movement time was longer and mean and peak velocity were lower for individuals with stroke. The number of velocity peaks, representing smoothness, was also discriminative and significantly higher in both stroke groups (mild, moderate) compared to controls. Movement trajectories in stroke more frequently showed clustering (spider’s web) close to the target indicating deficits in movement precision.ConclusionThe target-to-target pointing task can provide valuable and specific information about sensorimotor impairment of the upper limb following stroke that might not be captured using traditional clinical scale.Trial registration detailsThe trial was registered with register number NCT01115348 at , on May 4, 2010. URL: .

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

“…A movement segment was defined as time when a target appears to time when it disappears. Five kinematic variables were calculated: movement time, mean velocity, peak velocity, time to peak velocity and smoothness [26].…”

“…This measure distinguishes between the relative time spent during the first visually triggered outward movement until the peak velocity is reached (feedforward), and the second half of the movement toward the target that requires more precision in order to touch the target (feedback). In the second half of the movement, subjects tend to make multiple attempts to reach the target, resulting in the formation of spider-web like patterns in the movement trajectory [26]. The smoothness metric was calculated by counting the number of velocity peaks in a movement segment.…”

Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals

“…The age of the included subjects was 59 [40,53,69,88] years (median [minimum, 25 th -percentile, 75 th -percentile, maximum]) with 14 of them being female. FMA-UE scores for the most affected and less affected sides were 49 [32,40,57,61] and 65 [56,63,66,66], respectively. ARAT scores for the most affected and less affected sides were 47 [30,39,55,57] and 57 [45,57,57,57] , respectively.…”

“…While these approaches are promising to relate sensorimotor impairments and activity limitations and further also allow to study compensatory trunk movements, the solutions rely on a costly and time-consuming measurement setup with an optical motion capture system, thereby having limited clinical applicability. Research towards more rapidly applicable approaches has also been proposed, for example relying on the same robotic end-effector as the VPIT [57,58]. However, the presented task did not require any precise object manipulations and relied on the regular handle of the end-effector that cannot record grip forces.…”

Clinical Application of Virtual Reality for Upper Limb Motor Rehabilitation in Stroke: Review of Technologies and Clinical Evidence

“…Viau et al reported that patients with hemiparesis used less wrist extension and more elbow extension at the end of the placing phase during reaching, grasping, and performing tasks in VR than in a real environment [ 74 ]. Similarly, several studies using reaching tasks also demonstrated that the movements in VR using HMDs were slower than those in the real environment and that spatial and temporal kinematics differ between VR and real environments [ 75 , 76 , 77 ]. Lott et al reported that the range of the center of pressure during reaching in standing (usually used for balance training) was different between real environments, non-immersive VR with 2D flat-screen displays, and immersive VR with HMDs [ 78 ].…”