As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms of costs, accessibility and a lack of therapists. This paper presents Immersive Neurorehabilitation Exercises Using Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system using virtual reality. The system is based on a thorough research methodology and is able to capture real-time user movements and evaluate joint mobility for both upper and lower limbs, record training sessions and save electromyography data. The use of the first-person perspective increases immersion, and the joint range of motion is calculated with the help of both the HTC Vive system and inverse kinematics principles applied on skeleton rigs. Tutorial exercises are demonstrated by a virtual therapist, as they were recorded with real-life physicians, and sessions can be monitored and configured through tele-medicine. Complex movements are practiced in gamified settings, encouraging self-improvement and competition. Finally, we proposed a training plan and preliminary tests which show promising results in terms of accuracy and user feedback. As future developments, we plan to improve the system’s accuracy and investigate a wireless alternative based on neural networks.

The worldwide increase in the number of disorders requiring rehabilitation is weighing more and more on healthcare systems, seriously affecting the quality of life of patients. Emergent technologies and techniques should be used more and more in both physical and psychological rehabilitation, after a thorough study of their potential and effects. Our paper presents an original virtual reality-based system including gamified immersive physio-psychological exercises, which was tested in a clinical setting with 25 patients suffering from various musculoskeletal, neuromotor, or mental disorders. A thorough testing protocol was followed during a two-week period, including repeated trials, progress tracking, and objective and subjective instruments used for data collection. A statistical analysis helped us identify interesting correlations between complex virtual reality games and people’s performance, and the high level of relaxation and stress relief (4.57 out of 5 across all games) which can be offered by VR-based psychotherapy exercises, as well as the increased ease of use (4.26 out of 5 perceived across all games) of properly designed training exercises regardless of patients’ level of VR experience (84% of patients with no or low experience and no patient with high experience).

As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms of costs, accessibility and a lack of therapists. This paper presents Immersive Neurorehabilitation Exercises Using Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system using virtual reality. The system is based on a thorough research methodology and is able to capture real-time user movements and evaluate joint mobility for both upper and lower limbs, record training sessions and save electromyography data. The use of the first-person perspective increases immersion, and the joint range of motion is calculated with the help of both the HTC Vive system and inverse kinematics principles applied on skeleton rigs. Tutorial exercises are demonstrated by a virtual therapist, as they were recorded with real-life physicians, and sessions can be monitored and configured through tele-medicine. Complex movements are practiced in gamified settings, encouraging self-improvement and competition. Finally, we proposed a training plan and preliminary tests which show promising results in terms of accuracy and user feedback. As future developments, we plan to improve the system's accuracy and investigate a wireless alternative based on neural networks.