Gamification of Upper Limb Rehabilitation in Mixed-Reality Environment

Abstract

The advancements in mixed reality (MR) technology in recent years have provided us with excellent prospects for creating novel approaches to supplement conventional physiotherapy to maintain a sufficient quantity and quality of rehabilitation. The use of MR systems to facilitate patients’ participation in intensive, repetitive, and task-oriented practice using cutting-edge technologies to enhance functionality and facilitate recovery is very encouraging. Multiple studies have found that patients who undergo therapy using MR experience significant improvements in upper limb function; however, assessing the efficacy of MR is challenging due to the wide variety of methods and tools used. Because of these challenges, a novel approach, gamified MR-based solution for upper extremity rehabilitation, is proposed, which is an MR application for the Microsoft HoloLens 2, complete with game levels, and can measure the ranges of motion of the arm joints. The proposed rehabilitative system’s functionality and usability were evaluated with ten healthy adult participants with no prior arm-related injuries and two occupational therapists (OTs). The system successfully provided rehab exercises for upper limb injuries through interactive mixed-reality games. The system can mimic upper limb behavior without additional sensors during rehab sessions. Unlike previously researched technologically-based rehabilitation methods, this method can integrate arm–joint data within the application and are independent of one another. The results and comparisons show that this system is relevant, accurate, and superior to previous VR-based rehabilitation methods because the VR-based system is blind to the surroundings, whereas the proposed approach has spatial awareness of the environment.

Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments

“…Another aspect of such systems is the presence of refined sensors to collect kinematic measurements (i.e., upper-limb and ocular movements) and quantify the quality of patients’ recovery ( Leocani et al, 2007 ; Pillai et al, 2022 ; Tada et al, 2022 ; Herrera et al, 2023 ). Soares et al (2021) evaluated the hand tracking accuracy of HTC Vive (immersive VR headset) and HoloLens2 (XR headset) with respect to a Motion Capture (MoCap) system, while Pascucci et al (2022) and Pillai et al (2022) compared the use of a Microsoft Kinect-based prototype and HoloLens2. These studies revealed the suitability of VR and XR headsets to detect jerky behavior in kinematic data and unexpected hand movements, and to precisely evaluate movement tracking.…”