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.

Recently many studies have shown the effectiveness of using augmented reality (AR) and virtual reality (VR) in biomedical image analysis. However, they are not automating the COVID level classification process. Additionally, even with the high potential of CT scan imagery to contribute to research and clinical use of COVID-19 (including two common tasks in lung image analysis: segmentation and classification of infection regions), publicly available data-sets are still a missing part in the system care for Algerian patients. This article proposes designing an automatic VR and AR platform for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic data analysis, classification, and visualization to address the above-mentioned challenges including (1) utilizing a novel automatic CT image segmentation and localization system to deliver critical information about the shapes and volumes of infected lungs, (2) elaborating volume measurements and lung voxel-based classification procedure, and (3) developing an AR and VR user-friendly three-dimensional interface. It also centered on developing patient questionings and medical staff qualitative feedback, which led to advances in scalability and higher levels of engagement/evaluations. The extensive computer simulations on CT image classification show a better efficiency against the state-of-the-art methods using a COVID-19 dataset of 500 Algerian patients. The developed system has been used by medical professionals for better and faster diagnosis of the disease and providing an effective treatment plan more accurately by using real-time data and patient information.

The World Health Organization estimates that there were around 10 million deaths due to cancer in 2020, and lung cancer was the most common type of cancer, with over 2.2 million new cases and 1.8 million deaths. While there have been advances in the diagnosis and prediction of lung cancer, there is still a need for new, intelligent methods or diagnostic tools to help medical professionals detect the disease. Since it is currently unable to detect at an early stage, speedy detection and identification are crucial because they can increase a patient’s chances of survival. This article focuses on developing a new tool for diagnosing lung tumors and providing thermal touch feedback using virtual reality visualization and thermal technology. This tool is intended to help identify and locate tumors and measure the size and temperature of the tumor surface. The tool uses data from CT scans to create a virtual reality visualization of the lung tissue and includes a thermal display incorporated into a haptic device. The tool is also tested by touching virtual tumors in a virtual reality application. On the other hand, thermal feedback could be used as a sensory substitute or adjunct for visual or tactile feedback. The experimental results are evaluated with the performance comparison of different algorithms and demonstrate that the proposed thermal model is effective. The results also show that the tool can estimate the characteristics of tumors accurately and that it has the potential to be used in a virtual reality application to “touch” virtual tumors. In other words, the results support the use of the tool for diagnosing lung tumors and providing thermal touch feedback using virtual reality visualization, force, and thermal technology.

Background: This paper presents a novel evaluation method for assessing the effectiveness of virtual reality serious games in post-stroke rehabilitation. Stroke, which is a leading cause of long-term disability, requires effective rehabilitation approaches to facilitate motor recovery and enhance patients’ quality of life. The study aims to investigate the potential benefits of incorporating VR technology into upper limb rehabilitation for post-stroke patients, with a specific focus on evaluating patient motivation and engagement. The proposed evaluation method provides insights into the effectiveness of VR serious games as a rehabilitation tool and contributes to the understanding of their impact on post-stroke recovery. Methods: This study involved the recruitment of a diverse sample of 20 patients, both male and female, who had experienced left hemiplegia as a result of an ischemic stroke. To ensure a representative sample, patients of different ages were included. Participants were divided into three groups, with each group invited to participate in different stages over a one-year period. During an eight-week period, all groups participated in a rehabilitation program consisting of virtualreality exercises twice a week, alongside five traditional rehabilitation sessions per week. Furthermore, this study introduces a novel approach using camera-based sensors to evaluate patient engagement in virtual reality exercises. By monitoring and analyzing facial expressions throughout each rehabilitation session, our objective is to leverage emotion recognition technology to understand patients’emotional states and levels of motivation and engagement. Results: The findings from this study indicate that a significant number of patients showed motivation during virtual reality rehabilitation exercises. Conclusion: The analysis of facial expressions utilizing camera-based sensors, yielded valuable insights into the emotional state and level of engagement exhibited by the participants. These findings substantiate the efficacy of virtual reality as a potent tool in fostering motivation among post-stroke patients, thereby actively involving them in rehabilitation exercises and facilitating their journey toward recovery.