English

Immersive Virtual Reality and Robotics for Upper Extremity Rehabilitation

Human-Computer Interaction 2023-07-03 v2 Robotics

Abstract

Stroke patients often experience upper limb impairments that restrict their mobility and daily activities. Physical therapy (PT) is the most effective method to improve impairments, but low patient adherence and participation in PT exercises pose significant challenges. To overcome these barriers, a combination of virtual reality (VR) and robotics in PT is promising. However, few systems effectively integrate VR with robotics, especially for upper limb rehabilitation. This work introduces a new virtual rehabilitation solution that combines VR with robotics and a wearable sensor to analyze elbow joint movements. The framework also enhances the capabilities of a traditional robotic device (KinArm) used for motor dysfunction assessment and rehabilitation. A pilot user study (n = 16) was conducted to evaluate the effectiveness and usability of the proposed VR framework. We used a two-way repeated measures experimental design where participants performed two tasks (Circle and Diamond) with two conditions (VR and VR KinArm). We observed no significant differences in the main effect of conditions for task completion time. However, there were significant differences in both the normalized number of mistakes and recorded elbow joint angles (captured as resistance change values from the wearable sleeve sensor) between the Circle and Diamond tasks. Additionally, we report the system usability, task load, and presence in the proposed VR framework. This system demonstrates the potential advantages of an immersive, multi-sensory approach and provides future avenues for research in developing more cost-effective, tailored, and personalized upper limb solutions for home therapy applications.

Keywords

Cite

@article{arxiv.2304.11110,
  title  = {Immersive Virtual Reality and Robotics for Upper Extremity Rehabilitation},
  author = {Vuthea Chheang and Rakshith Lokesh and Amit Chaudhari and Qile Wang and Lauren Baron and Behdokht Kiafar and Sagar Doshi and Erik Thostenson and Joshua Cashaback and Roghayeh Leila Barmaki},
  journal= {arXiv preprint arXiv:2304.11110},
  year   = {2023}
}

Comments

9 pages, 6 figures