Can We Use the Oculus Quest VR Headset and Controllers to Reliably Assess Balance Stability?

Development of a new immersive virtual reality (VR) headset-based dexterity training for patients with multiple sclerosis: Clinical and technical aspects

BACKGROUND

Impaired manual dexterity is frequent and disabling in patients with multiple sclerosis (MS), affecting activities of daily living and quality of life.

OBJECTIVE

To develop a new immersive virtual-reality (VR) headset-based dexterity training to improve impaired manual dexterity in persons with MS (pwMS) while being feasible and usable in a home-based setting.

METHODS

The training intervention was tailored to the specific group of pwMS by implementing a simple and intuitive application with regard to hardware and software. To be efficacious, the training intervention covers the main functions of the hands and arm relevant for use in everyday life.

RESULTS

Taking clinical, feasibility, usability as well as technical aspects with regard to hardware and software into account, six different training exercises using hand tracking technology were developed on the Meta quest 2 using Unity.

CONCLUSION

We report the developmental process of a new immersive virtual VR headset-based dexterity training for pwMS implementing clinical and technical aspects. Good feasibility, usability, and patient satisfaction was already shown in a feasibility study qualifying this training intervention for further efficacy trials.

Quantitative Comparison of Hand Kinematics Measured with a Markerless Commercial Head-Mounted Display and a Marker-Based Motion Capture System in Stroke Survivors

Upper-limb paresis is common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients' movements in ecological scenarios. These systems are, however, very expensive and not readily available for many rehabilitation units. Here, we explored whether the markerless hand motion capabilities of the cost-effective Oculus Quest head-mounted display could be used to provide clinically meaningful measures. A total of 14 stroke patients executed ecologically relevant upper-limb tasks in an immersive virtual environment. During task execution, we recorded their hand movements simultaneously by means of the Oculus Quest's and a marker-based motion capture system. Our results showed that the markerless estimates of the hand position and peak velocity provided by the Oculus Quest were in very close agreement with those provided by a marker-based commercial system with their regression line having a slope close to 1 (maximum distance: mean slope = 0.94 ± 0.1; peak velocity: mean slope = 1.06 ± 0.12). Furthermore, the Oculus Quest had virtually the same sensitivity as that of a commercial system in distinguishing healthy from pathological kinematic measures. The Oculus Quest was as accurate as a commercial marker-based system in measuring clinically meaningful upper-limb kinematic parameters in stroke patients.

Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices

Significant advances in sensor technology and virtual reality (VR) offer new possibilities for early and effective detection of mild cognitive impairment (MCI), and this wealth of data can improve the early detection and monitoring of patients. In this study, we proposed a non-invasive and effective MCI detection protocol based on electroencephalogram (EEG), speech, and digitized cognitive parameters. The EEG data, speech data, and digitized cognitive parameters of 86 participants (44 MCI patients and 42 healthy individuals) were monitored using a wearable EEG device and a VR device during the resting state and task (the VR-based language task we designed). Regarding the features selected under different modality combinations for all language tasks, we performed leave-one-out cross-validation for them using four different classifiers. We then compared the classification performance under multimodal data fusion using features from a single language task, features from all tasks, and using a weighted voting strategy, respectively. The experimental results showed that the collaborative screening of multimodal data yielded the highest classification performance compared to single-modal features. Among them, the SVM classifier using the RBF kernel obtained the best classification results with an accuracy of 87%. The overall classification performance was further improved using a weighted voting strategy with an accuracy of 89.8%, indicating that our proposed method can tap into the cognitive changes of MCI patients. The MCI detection scheme based on EEG, speech, and digital cognitive parameters proposed in this study provides a new direction and support for effective MCI detection, and suggests that VR and wearable devices will be a promising direction for easy-to-perform and effective MCI detection, offering new possibilities for the exploration of VR technology in the field of language cognition.

Feasibility and usability of a new home-based immersive virtual reality headset-based dexterity training in multiple sclerosis

BACKGROUND

Impaired manual dexterity is frequent and disabling in patients with multiple sclerosis (MS), affecting activities of daily living and quality of life.

OBJECTIVE

The aim of this study was to evaluate the feasibility, usability and patient engagement/satisfaction of a home-based immersive virtual reality (VR) headset-based dexterity training in persons with multiple sclerosis (pwMS). In addition, preliminary efficacy data on the impact of this new training on manual dexterity were collected.

METHODS

Single arm prospective study. After a waiting period of two weeks, pwMS performed a specifically developed home-based VR headset-based dexterity training using the Oculus quest 2 for two weeks with five training sessions/week, each session for approximately 20 minutes. Primary endpoints were feasibility (measured by the adherence rate), usability (System Usability Scale, SUS) and patient engagement/satisfaction (Custom User Engagement Questionnaire, CUEQ). Secondary exploratory efficacy endpoints, measured before and after the waiting period as well as after the training intervention, were the Nine-hole-Peg-Test (9HPT), Coin rotation task (CRT), Handheld JAMAR dynamometer, Arm Function in Multiple Sclerosis Questionnaire (AMSQ) and the Multiple Sclerosis Impact Scale 29 (MSIS 29).

RESULTS

Eleven pwMS (mean age 49 ± 10.87 SD, mean EDSS 4.28 ± 1.48 SD) participated in the study. Feasibility (adherence rate: 81.8%), usability (median SUS score 94 (IQR = 78-96)) and patient engagement/satisfaction (median 8 on scale of 1-10) of the VR training was very high. In addition, the CRT for the dominant hand improved significantly after training (p = 0.03).

CONCLUSIONS

The good results on feasibility, usability, and patient engagement/satisfaction qualify this home-based immersive VR headset-based dexterity training approach for the use in home-based neurorehabilitation in pwMS. Improved fine motor skills for the dominant hand suggest preliminary efficacy, but this needs to be proven in a future randomized-controlled trials.

Gamification in Therapeutic Rehabilitation of Distal Radial and Ulnar Fracture: A Case Report

Gamification is a novel interventional approach to functional recovery and rehabilitation. We present a novel gamification rehabilitation case of a 38-year-old female referred to the physiotherapy department for post-distal radial fracture rehabilitation. She was managed with closed reduction and internal fixation (CRIF) using two Kirschner wires (K-wires) after falling on an outstretched hand in a road traffic accident with a two-wheeler. Gamification intervention was provided to the patient for four weeks. We used pre- and post-intervention outcome measures for pain, range of motion, grip strength, and hand function. There was a substantial improvement in all outcome measures after four weeks of gamification intervention.