Motor rehabilitation exercises require recurrent repetitions to enhance patients’ gestures. However, these repetitive gestures usually decrease the patients’ motivation and stress them. Virtual Reality (VR) exer-games (serious games in general) could be an alternative solution to address the problem. This innovative technology encourages patients to train different gestures with less effort since they are totally immersed in an easy to play exer-game. Despite this evolution, patients, with available exer-games, still suffer in performing their gestures correctly without pain. The developed applications do not consider the patients psychological states when playing an exer-game. Therefore, we believe that is necessary to develop personalized and adaptive exer-games that take into consideration the patients’ emotions during rehabilitation exercises. This paper proposed a VR-PEER adaptive exer-game system based on emotion recognition. The platform contain three main modules: (1) computing and interpretation module, (2) emotion recognition module, (3) adaptation module. Furthermore, a virtual reality-based serious game is developed as a case study, that uses updated facial expression data and provides dynamically the patient’s appropriate game to play during rehabilitation exercises. An experimental study has been conducted on fifteen subjects who expressed the usefulness of the proposed system in motor rehabilitation process.

In this paper, software architecture model “MVC-3DC” for Collaborative Augmented and Virtual Reality Systems design is proposed. This model is the results of merging several aspects: Human-Computer Interaction (HCI), distribution systems, computer-supported cooperative work (CSCW) and new technologies such as augmented reality and virtual reality. MVC-3DC integrates collaboration principles between remote users. MVC-3DC allows a low dependency between components such as the core functions, 3D graphics API and data distribution modes. The proposed architectural model integrates simulation models, SDKs and algorithms for different nodes involved in a collaborative session. This facilitates interoperability and capability to manage heterogeneity and relationship between different nodes participating in the collaborative session. Finally, our model makes it possible to integrate other toolkits without completely changing the structure of collaboration model. A simple adaptation could be made.