Non-photorealistic rendering as a feedback strategy in virtual reality for rehabilitation

Abstract

Virtual reality (VR) rehabilitation has been proven to be a very promising method to increase the focus and attention of patients by immersing them in a virtual world, and through that, improve the effectiveness of the rehabilitation. One of the biggest challenges in designing VR Rehabilitation exercises is in choosing feedback strategies that guide the patient and give the appropriate success/failure indicators, without breaking their sense of immersion. A new strategy for feedback is proposed, using non-photorealistic rendering (NPR) to highlight important parts of the exercise the patient needs to focus on and fade out parts of the scene that are not relevant. This strategy is implemented into an authoring tool that allows rehabilitators specifying feedback strategies while creating exercise profiles. The NPR feedback can be configured in many ways, using different NPR schemes for different layers of the exercise environment such as the background environment, the non-interactive exercise objects, and the interactive exercise objects. The main features of the system including the support for universal render pipeline, camera stacking, and stereoscopic rendering are evaluated in a testing scenario. Performance tests regarding memory usage and supported frames per second are also considered. In addition, a group of rehabilitators evaluated the system usability. The proposed system meets all the requirements to apply NPR effect in VR scenarios and solves all the limitations with regard to technical function and image quality. In addition, the system performance has been shown to meet the targets for low-cost hardware. Regarding authoring tool usability rehabilitators agree that is easy to use and a valuable tool for rehabilitation scenarios. NPR schemes can be integrated into VR rehabilitation scenarios achieving the same image quality as non-VR visualizations with only a small impact on the frame rate. NPR schemes are a good visual feedback alternative.