NAMI: A Neuro-Adaptive Multimodal Architecture for Wearable Human–Computer Interaction

The increasing ubiquity of wearable computing and multimodal interaction technologies has created unprecedented opportunities for natural and seamless human–computer interaction. However, most existing systems adapt only to external user actions such as speech, gesture, or gaze, without considering internal cognitive or affective states. This limits their ability to provide intelligent and empathetic adaptations. This paper addresses this critical gap by proposing the Neuro-Adaptive Multimodal Architecture (NAMI), a principled, modular, and reproducible framework designed to integrate behavioral and neurophysiological signals in real time. NAMI combines multimodal behavioral inputs with lightweight EEG and peripheral physiological measurements to infer cognitive load and engagement and adapt the interface dynamically to optimize user experience. The architecture is formally specified as a three-layer pipeline encompassing sensing and acquisition, cognitive–affective state estimation, and adaptive interaction control, with clear data flows, mathematical formalization, and real-time performance on wearable platforms. A prototype implementation of NAMI was deployed in an augmented reality Java programming tutor for postgraduate informatics students, where it dynamically adjusted task difficulty, feedback modality, and assistance frequency based on inferred user state. Empirical evaluation with 100 participants demonstrated significant improvements in task performance, reduced subjective workload, and increased engagement and satisfaction, confirming the effectiveness of the neuro-adaptive approach.