ABSTRACT
BACKGROUND: Compensatory movements are commonly observed in older adults with stroke during upper extremity (UE) motor rehabilitation, which could limit their motor recovery. AIM: This study aims to develop a compensation-aware virtual rehabilitation system (VRS) that can detect compensatory movements and improve the outcome of UE rehabilitation in community-dwelling older adults with stroke. METHODS: The VRS development includes three main components: (1) the use of thresholds for determining compensatory movements, (2) the algorithm for processing the kinematic data stream from Kinect to detect compensation in real-time, and (3) the audio-visual feedback to assist older adults with stroke to be aware of the compensation. Two studies were conducted following the VRS development, where Study 1 identified the value of thresholds for determining compensatory movements in two planar motor exercises, and Study 2 provided preliminary validation for the developed VRS by comparing two groups undergoing VR training or conventional training (CT) in a community rehabilitation center. RESULTS: The VRS could effectively detect all determined compensatory movements and timely trigger feedback in response to the detected compensatory movements. The VR participants showed significant improvements in Fugl-Meyer Assessment-Upper Extremity (FMA-UE, p = 0.045) and Wolf Motor Function Test (WMFT, p = 0.009). However, the VR and CT groups had no significant differences in outcome measures. CONCLUSION: The VRS demonstrates the ability to detect compensation and the potential of assisting older adults with stroke to improve motor functions. Suggestions are given for further improvements of the VRS to support the older adult with stroke to reduce compensation.
