Title: AI, Innovation and Global Transformation: Interdisciplinary Perspectives on Technology, Business and Society
Editors: Dr. J. Preetha, and Dr. Siddhartha Mehrotra
ISBN: 978-81-69857-64-2
Chapter: 26
DOI: https://doi.org/10.59646/809/26
Authors: Prof. Pankaj Ramdas Bhusari, Prof. Prakash Shankar Andhare, Prof. Patil Jyoti Kamalakar, and Prof. Prashant Sopan Ingale
Abstract
Human cognition operates by seamlessly binding sensory stimuli from vision, speech, physiological rhythms, and contextual linguistic cues into coherent perceptual experiences. Conventional unimodal artificial intelligence models often fail to emulate this holistic integration, remaining brittle to partial channel degradation, noise, and cross-modal ambiguity. This chapter presents an end-to-end multimodal artificial intelligence framework designed to enable intelligent, adaptive, and human-centered computing environments. By leveraging unified transformer architectures, cross-attention mechanism representations, and continuous physiological sensing, the proposed methodology captures fine-grained human emotional and cognitive dynamics. The architecture balances cross-modal representation alignment with missing-modality imputation strategies to dynamically adapt human-machine interfaces across varying user fatigue, cognitive workload, and stress levels. Evaluated on a multimodal dataset comprising video, audio, electroencephalography, and peripheral autonomic signals, our approach demonstrates resilience, perceptual latency reduction, and high classification accuracy. The findings illustrate a viable trajectory for trustworthy, responsive, and ergonomic human-agent symbiosis across assistive health, intelligent workstations, and adaptive collaborative computing systems.
Keywords: Multimodal Artificial Intelligence; Human-Centered Computing; Adaptive Systems; Cross-Modal Attention; Cognitive Workload Estimation; Affective Computing.