Date Presented 03/26/20
This research provides an exploration into the use of virtual reality–based tools for assessment in stroke. As these tools become more widely used in practice, it is imperative that OT practitioners are able to utilize data effectively in clinical decision-making.
Primary Author and Speaker: Rachel Proffitt
Contributing Authors: Mengxuan Ma, Marjorie Skubic
PURPOSE: Nearly 85% of individuals survive the initial event of a stroke and return to their homes and communities with about half of individuals reporting hemiparesis affecting full participation in daily activities (Benjamin et al., 2019). Home-based programs are necessary to deliver additional hours of therapy outside of the clinic. Virtual reality (VR)-based approaches show modest outcomes in improving client function (Laver et al., 2017) when delivered in the home. The movement sensors used in these VR-based approaches, such as the Microsoft Kinect® have been validated against gold standards tools (Ma et al., 2018) but have not been used as an assessment of upper extremity movement in the stroke population. The purpose of this study was to explore the use of a movement sensor paired with a VR-based intervention to assess upper extremity movement for individuals post-stroke.
DESIGN: Movement data from four separate studies were aggregated for analysis (n = 8 individuals post-stroke, n = 30 individuals without disabilities).
METHOD: The individuals post-stroke all had hemiparesis, were able to follow 2-step commands, were at least 6 months post-stroke, and could view a TV screen from 6 feet with or without visual correction. The eight individuals post-stroke played the Mystic Isle game with the Microsoft Kinect® in their home for 6-8 weeks. The games were customized to their goals identified using the Canadian Occupational Performance Measure. Games involved reaching and balance tasks in the virtual environment. The healthy individuals had no upper extremity deficits and had not experienced a stroke. They all completed 12 short customized games in a laboratory setting that involved reaching and balance. For all participants, the skeletal data (x, y, z coordinates for 20 tracked joints) for each game play session were processed in MatLab and movement variables (normalized jerk, movement path ratio, average path sway) were calculated using an OPTICS density-based cluster algorithm.
RESULTS: Data from the 30 healthy individuals created a normative baseline for the 3 kinematic variables. A t-test revealed no statistically significant differences between right and left extremities (p = 0.592) for health individuals so data were collapsed. For healthy individuals, normalized jerk was 1.8e07 ± 6.9e07, movement path ratio 0.69 ± 0.2, and average path sway (mm) 109.3 ± 76.4. For the more affected upper extremity of individuals post-stroke, normalized jerk was 5.0e09 ± 2.3e10, movement path ratio 0.48 ± 0.2, and average path sway (mm) 150.3 ± 100.7. For the less affected upper extremity of individuals post-stroke, normalized jerk was 4.8e09 ± 2.410, movement path ratio 0.52 ± 0.2, and average path sway (mm) 136.3 ± 92.3. An ANOVA with post-hoc Bonferonni correction revealed that there were statistically significant differences between individuals post-stroke and healthy individuals (all p = 0.000). Additionally, there were no statistically significant differences in normalized jerk (p = 0.992) and average path sway (p = 0.014) between the more and less affected upper extremities of individuals post-stroke.
CONCLUSION: It is feasible to use a movement sensor paired with a VR-based intervention to quantify upper extremity movement for individuals post-stroke. These kinematic measures are sensitive and able to detect differences from normative values. Further research with a larger cohort is necessary to establish clinical sensitivity and specificity.
IMPACT STATEMENT: This research provide an exploration into the use of VR for assessment in stroke. As these tools become more widely used in practice, it is imperative that OT practitioners are able to utilize data effectively in clinical decision-making.
References
Benjamin, E. J., Muntner, P., & Bittencourt, M. S. (2019). Heart disease and stroke statistics-2019 update: A report from the American Heart Association. Circulation, 139(10), e56-e528. Doi: https://doi.org/10.1161/CIR.0000000000000659.
Laver, K. E., Lange, B., George, S., Deutsch, J. E., Saposnik, G., & Crotty, M. (2017). Virtual reality for stroke rehabilitation. Cochrane database of systematic reviews, (11). https://doi.org/10.1002/14651858.CD008349.pub4.
Ma, M., Proffitt, R., & Skubic, M. (2018). Validation of a Kinect V2 based rehabilitation game. PloS one, 13(8), e0202338. Doi: https://doi.org/10.1371/journal.pone.0202338.
