The rising incidence of stroke has created a significant global public health challenge. The immersive qualities of virtual reality (VR) technology, along with its distinct advantages, make it a promising tool for stroke rehabilitation. To address this challenge, developing VR-based upper limb rehabilitation systems has become a critical research focus. This study developed and evaluated an innovative ArmVR system that combines VR technology with rehabilitation hardware to improve recovery outcomes for stroke patients. Through comprehensive assessments, including neurofeedback, pressure feedback, and subjective feedback, the results suggest that VR technology has the potential to positively support the recovery of cognitive and motor functions. Different VR environments affect rehabilitation outcomes: forest scenarios aid emotional relaxation, while city scenarios better activate motor centers in stroke patients. The study also identified variations in responses among different user groups. Normal users showed significant changes in cognitive function, whereas stroke patients primarily experienced motor function recovery. These findings suggest that VR-integrated rehabilitation systems possess great potential, and personalized design can further enhance recovery outcomes, meet diverse patient needs, and ultimately improve quality of life.

Studies have shown that prolonged Human-Computer Interaction (HCI) fatigue can increase the risk of mental illness and lead to a higher probability of errors and accidents during operations. Virtual Reality (VR) technology can simultaneously stimulate multiple senses such as visual, auditory, and tactile, providing an immersive experience that enhances cognition and understanding. Therefore, this study collects multimodal data to develop evaluation methods for HCI fatigue and further explores the fatigue-relieving effects of VR technology by comparing it with flat video. Using a modular design, electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) data in the resting, fatigue-induced, and recovery states, eye movement data in the resting and fatigue-induced states, as well as subjective scale results after each state were collected from the participants. Preprocessing and statistical analysis are performed through data flow architecture. After fatigue induction, it was found that the degree of activation of brain areas, especially the Theta band of prefrontal cortex, occurred significantly higher, the effective connectivity in the Alpha and Theta bands occurred significantly lower, the subjects' pupil diameters decreased, the blink frequency increased, and subjective questionnaire scores increased, which verified the validity of the multimodal data for assessing HCI fatigue. Analyzing fatigue relief through subgroups, it was found that when using the natural grassland scene with soothing music, both flat video and VR had the ability to alleviate fatigue, which was manifested as a significant decrease in the Alpha band in the LPFC brain area and a decrease in the questionnaire score. Moreover, during the recovery state, it was found that compared to the video group, the VR group had significantly higher activation in the Alpha and Theta bands of the prefrontal cortex, while the video group had significantly higher effective connectivity than the VR group in the Alpha band. This study delved deeply into the multidimensional characterization of fatigue and investigated new scenarios for the use of VR, which can help to promote the use of VR and can be migrated to scenarios that require fatigue management and productivity enhancement.

With the growing needs on rehabilitation of the mild cognitive impairment (MCI) users group and the advantages of virtual reality (VR) technologies in cognitive training, the development of VR-based rehabilitation training methods has become a hot spot recently. However, the challenges in accurately measuring users’ needs and quantifying training system efficacy are still not well resolved, especially for longitudinal tracking. In this study, a VR-based cognitive training and evaluation system is designed and implemented, targeting at fulfilling the rehabilitation needs of MCI users. It evaluates the impact of longitudinal VR-based training on MCI users with a number of feedback methodologies including brain activation indicators, brain network connectivity indicators, behavioral indicators and the Montreal Cognitive Assessment (MoCA) scale scores, extracted from multi-modal data collected while training. A two-month longitudinal tracking ergonomics experiment was conducted to validate the usability of the feedback methodologies and to explore the influence of the training duration on the rehabilitation efficacy. The results showed that our proposed VR-based cognitive training and evaluation system had a positively significant impact on the rehabilitation of the MCI group. Meanwhile, the multi-source feedbacks can also help the updates and iterations of VR-based rehabilitation training systems. Finally, this study provides guidance for the selection of rehabilitation cycles and emphasizes the importance of quantitative studies with longitudinal follow-up in assessing rehabilitation efficacy.