Novel clinically-relevant assessment of upper extremity movement using depth sensors

ABSTRACT

Background

For individuals post-stroke, home-based programs are necessary to deliver additional hours of therapy outside of the limited time in the clinic. Virtual reality (VR)-based approaches show modest outcomes in improving client function 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 but have not been used as an assessment of upper extremity movement quality in the stroke population.

Objectives

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.

Methods

Movement data captured with the Microsoft Kinect® from four separate studies were aggregated for analysis (n = 8 individuals post-stroke, n = 30 individuals without disabilities). For all participants, the skeletal data (x, y, z coordinates for 15 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 three kinematic variables. Individuals post-stroke were less efficient and had more jerky movements in both upper extremities as compared to healthy individuals.

Conclusion

It is feasible to use a movement sensor paired with a VR-based intervention to quantify and qualify upper extremity movement for individuals post-stroke. Further research with a larger cohort is necessary to establish clinical sensitivity and specificity.

KEYWORDS:

• Stroke
• computer games
• upper extremity
• kinematics
• occupational therapy

Disclosure statement

The authors have nothing to disclose.

Data availability

The data set associated with this publication can be located at https://hdl.handle.net/10355/78942.

Additional information

Funding

This work was supported by the Telemedicine and Advanced Technology Research Center (TATRC) at the US Army Medical Research and Materiel Command(USAMRMC) (W911NF-04-D-0005) (PI: Lange); by Grant Number UL1 TR000448 from the NIH National Center for Advancing Translational Sciences (NCATS), components of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research; by the Mizzou Alumni Association University of Missouri Richard Wallace Faculty Incentive Grant; and by a small project award from LSVT®Global;LSVT Global[Small Project Award];