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Using Capability Maps Tailored to Arm Range of Motion in VR Exergames for Rehabilitation

Christian Lourido, Zaid Waghoo, Hassam Khan Wazir, Nishtha Bhagat, Vikram Kapila

TL;DR

This work addresses upper-limb rehabilitation after neurological injury by integrating a 7-DoF arm kinematic model with capability maps to quantify each user’s reachable workspace and dexterity. An offline process generates two capability maps (healthy and shoulder-restricted) that inform online VR exergames: ROM improvement and occupational therapy tasks. ROM is measured with a Kinect to produce joint ROM and limb lengths feeding into the model, enabling tailored balloon-popping exergames and ADL-mimicking OT cues guided by reachability and convex-hull visualizations. Preliminary testing with four participants demonstrates measurable changes in workspace and performance as shoulder restrictions vary, highlighting the framework’s potential to provide engaging, objective, and personalized rehabilitation with real-time capability-aware adaptations.

Abstract

Many neurological conditions, e.g., a stroke, can cause patients to experience upper limb (UL) motor impairments that hinder their daily activities. For such patients, while rehabilitation therapy is key for regaining autonomy and restoring mobility, its long-term nature entails ongoing time commitment and it is often not sufficiently engaging. Virtual reality (VR) can transform rehabilitation therapy into engaging game-like tasks that can be tailored to patient-specific activities, set goals, and provide rehabilitation assessment. Yet, most VR systems lack built-in methods to track progress over time and alter rehabilitation programs accordingly. We propose using arm kinematic modeling and capability maps to allow a VR system to understand a user's physical capability and limitation. Next, we suggest two use cases for the VR system to utilize the user's capability map for tailoring rehabilitation programs. Finally, for one use case, it is shown that the VR system can emphasize and assess the use of specific UL joints.

Using Capability Maps Tailored to Arm Range of Motion in VR Exergames for Rehabilitation

TL;DR

This work addresses upper-limb rehabilitation after neurological injury by integrating a 7-DoF arm kinematic model with capability maps to quantify each user’s reachable workspace and dexterity. An offline process generates two capability maps (healthy and shoulder-restricted) that inform online VR exergames: ROM improvement and occupational therapy tasks. ROM is measured with a Kinect to produce joint ROM and limb lengths feeding into the model, enabling tailored balloon-popping exergames and ADL-mimicking OT cues guided by reachability and convex-hull visualizations. Preliminary testing with four participants demonstrates measurable changes in workspace and performance as shoulder restrictions vary, highlighting the framework’s potential to provide engaging, objective, and personalized rehabilitation with real-time capability-aware adaptations.

Abstract

Many neurological conditions, e.g., a stroke, can cause patients to experience upper limb (UL) motor impairments that hinder their daily activities. For such patients, while rehabilitation therapy is key for regaining autonomy and restoring mobility, its long-term nature entails ongoing time commitment and it is often not sufficiently engaging. Virtual reality (VR) can transform rehabilitation therapy into engaging game-like tasks that can be tailored to patient-specific activities, set goals, and provide rehabilitation assessment. Yet, most VR systems lack built-in methods to track progress over time and alter rehabilitation programs accordingly. We propose using arm kinematic modeling and capability maps to allow a VR system to understand a user's physical capability and limitation. Next, we suggest two use cases for the VR system to utilize the user's capability map for tailoring rehabilitation programs. Finally, for one use case, it is shown that the VR system can emphasize and assess the use of specific UL joints.
Paper Structure (13 sections, 4 figures, 2 tables)

This paper contains 13 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. METHODOLOGY
  3. Human Arm Kinematic Model
  4. Capability Map
  5. Rehabilitation Exergame
  6. ROM measurement
  7. VR exergame for ROM improvement
  8. VR exergame for OT
  9. SYSTEM EVALUATION
  10. Reachable volume and dexterity analysis
  11. Tailored ROM exergame
  12. Results of the tailored ROM exergame
  13. CONCLUSION AND FUTURE WORK

Figures (4)

  • Figure 1: (a) Arm kinematic model and (b) rigid-body tree with collision meshes.
  • Figure 2: Capability maps: (a) healthy user and (b) user wearing a restriction.
  • Figure 3: Exergame for ROM improvement (a) therapist view and (b) user view.
  • Figure 4: Exergame for OT (a) visual cues, (b) therapist view, and (c) user view.