Design and evaluation of AR-based adaptive human-computer interaction cognitive training

• We developed an AR-based adaptive HCI cognitive training system that dynamically adjusts the difficulty. • We evaluated the effects of adaptive cognitive training through the use of fNIRS . • The AR-based adaptive HCI strategies influence brain FC associated with cognition, movement, and vision. • Incorporating subjective and objective data can enhance the precision of assessments for HCI systems. As human-computer interaction (HCI) technology advances, the use of augmented reality (AR) in cognitive training is becoming more prevalent. However, traditional training methods often apply a one-size-fits-all approach, failing to accommodate the varied training needs of individuals with different cognitive levels. Additionally, most HCI systems use subjective questionnaires for evaluation, which can be influenced by the subjects' emotional and mental states. To overcome these challenges, this study developed an AR-based adaptive HCI cognitive training system that dynamically adjusts task difficulty based on real-time user performance. We used multi-source data to empirically validate the effectiveness of adaptive HCI in cognitive training. Specifically, we recorded functional Near-Infrared Spectroscopy (fNIRS) data, movement data, task performance, and subjective feedback from 22 elderly participants, dividing them into two groups—low cognitive group and normal cognitive group. The results showed that the system exerted a significant influence on brain functional connectivity (FC) associated with cognition, movement, and vision. Changes in FC may highlight the benefits of adaptive HCI training strategies. Furthermore, participants with normal cognitive abilities significantly outperformed their low cognitive counterparts in task performance. In conclusion, this study designed and evaluated an AR-based adaptive HCI cognitive training system that ensures personalized training. It demonstrated the feasibility of adaptive HCI strategies in cognitive rehabilitation by incorporating physiological and behavioral data, thereby enhancing the precision of quantitative assessments for HCI systems.