Advancing Trunk Control and Balance in Rehabilitation: A Quantitative Approach Using VR Head-Mounted Display and Motion-Sensing Technologies

Virtual reality (VR) technology is becoming increasingly vital across various sectors, including healthcare, engineering, and science. Its applications extend to training, education, clinical evaluations, and rehabilitation. Particularly in rehabilitation, VR is instrumental for assessing and treating a range of conditions. It holds promise for enhancing balance and gait in patients with neurological impairments and offers added benefits when integrated with conventional rehabilitation therapies. Despite its widespread use, there is a notable absence of methods and technologies for the functional quantification of training performance within current VR systems. This study addresses this gap by employing VR head-mounted displays and motion-sensing recognition technologies. This integration facilitates precise positioning and free movement within a VR environment, leveraging motion interaction functions to target trunk control and balance training. The study introduces a functional quantification framework that encompasses algorithms for analyzing movement trajectories, trunk activity, and three-dimensional spatial motion performance. This framework digitally records and updates the rehabilitation journey of participants in real-time, generating functional reports. Such reports enable medical professionals and patients to monitor rehabilitation progress continuously, thereby enhancing the overall efficacy of the treatment process.