Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation

Abstract

Background: Stroke is a frequent cause of adult disability that can lead to enduring impairments. However, given the life-long plasticity of the brain one could assume that recovery could be facilitated by the harnessing of mechanisms underlying neuronal reorganization. Currently it is not clear how this reorganization can be mobilized. Novel technology based neurorehabilitation techniques hold promise to address this issue. Here we describe a Virtual Reality (VR) based system, the Rehabilitation Gaming System (RGS) that is based on a number of hypotheses on the neuronal mechanisms underlying recovery, the structure of training and the role of individualization. We investigate the psychometrics of the RGS in stroke patients and healthy controls.

Methods: We describe the key components of the RGS and the psychometrics of one rehabilitation scenario called Spheroids. We performed trials with 21 acute/subacute stroke patients and 20 healthy controls to study the effect of the training parameters on task performance. This allowed us to develop a Personalized Training Module (PTM) for online adjustment of task difficulty. In addition, we studied task transfer between physical and virtual environments. Finally, we assessed the usability and acceptance of the RGS as a rehabilitation tool.

Results: We show that the PTM implemented in RGS allows us to effectively adjust the difficulty and the parameters of the task to the user by capturing specific features of the movements of the arms. The results reported here also show a consistent transfer of movement kinematics between physical and virtual tasks. Moreover, our usability assessment shows that the RGS is highly accepted by stroke patients as a rehabilitation tool.

Conclusions: We introduce a novel VR based paradigm for neurorehabilitation, RGS, which combines specific rehabilitative principles with a psychometric evaluation to provide a personalized and automated training. Our results show that the RGS effectively adjusts to the individual features of the user, allowing for an unsupervised deployment of individualized rehabilitation protocols.

Figures

Figure 1

The Rehabilitation Gaming System. A subject sits on a chair with his/her arms on a table, facing a screen. Arm movements are tracked by the camera mounted on top of the display (a). The tracking system determines in real-time the position of the color patches positioned at wrists and elbows and maps these onto a biomechanical model of the upper extremities (b). Data gloves can be used to detect finger movements (c). On the display two virtual arms mimic the movements of the subject's arms.

Figure 2

Spheroids and the virtual environment. The scenario represents a spring-like nature scenario. Within this scenario two virtual arms move accordingly to the movements of the user. The virtual arms are consistent with the orientation of the user, pointing towards the world, providing a first person perspective during the virtual interaction. The difficulty of the sphere interception task is modulated by the speed of the delivered spheres, the interval of appearance between consecutive spheres and the range of dispersion in the field of view. The gaming parameters are graphically described in the Figure.

Figure 3

Calibration task. The user has to move his/her hands to numbered dots positioned on a tabletop. (a) Coordinates (in cm) of the target numbers to be reached. (b) Physical calibration task. The task is performed on the physical tabletop. (c) Virtual calibration task. Virtual replica of the physical calibration task. The instructions are the same as in the real task, but now the task is to be performed with the virtual arms on top of the virtual table.

Figure 4

Flow diagram of the RGS Personalized Training Module. The game parameters are continuously updated based on the performance of the subject. This provides an automated adjustment of the difficulty of training over time based on a psychometrically validated user model.

Figure 5

Performance versus game parameters in control subjects. a) Performance as a function of Size and Speed; b) Performance as a function of Size and Interval; c) Performance as a function of Size and Range; d) Performance as a function of Interval and Speed; e) Performance as a function of Range and Speed; f) Performance as a function of Range and Interval. Performance is measured as the percentage of successful sphere interceptions.

Figure 6

Game events and task difficulty. (a) Arm reaching distance over time for paretic (red) and healthy (blue) arms, and corresponding game events (hit and missed spheres). (b) Difficulty curves for paretic (red) and healthy (blue) arms over trials.

Figure 7

Adaptive game results. Difficulty (a) and score (b) ratios between the paretic and the nonparetic arms for patients (light grey); and between the nondominant and dominant arms for controls (dark grey). (c-d) Relation between game parameters for individual arms. * p < .05. Shown are means ± SEM.

Figure 8

Movement speed in an equivalent real and virtual calibration task. Speed (mean ± SEM) for both arms, in controls and patients, in real and virtual environments. * p < .05, ** p < .01.

Figure 9

Movement trajectories in an equivalent real and virtual calibration task. Endpoint movement trajectories between two fixed points in the real and virtual calibration task for both arms, in controls and patients. Left arm movements are mirrored to the right side to allow for comparison.