With the evolution of Industry 4.0 and intelligent environments, efficient signal collection and processing are crucial to ensure system integration as automation systems are put at the heart of the process. In this study, we present a novel AI-based system to measure and authenticate behavioral signals and apply it to accelerator data obtained from a wrist-worn sensor. To counter the intricacies of nonintrusive monitoring, the proposed system integration employs variational mode decomposition (VMD) to refine the signal at multiple levels and Blackman-window segmentation to provide time consistency. The architecture uses a new AI application in signal processing through the adaptive combination of multimodal features such as gammatone cepstral and temporal moments using localized multiple kernel learning (LKML). Quantum-inspired Harris Hawk optimization is further used to optimize this process, and dynamic time warping aligns it. To attain the final classification in the automation loop, graph attention networks and temporal convolutional networks (TCNs) are merged to represent complex motion signatures. The framework is tested on the datasets Capture-24 and CAHAR with accuracies of 91.74 and 88.93%, respectively. The findings show considerable resistance to noise and time-varying effects, which contributes to the development of integrated engineering and the safe signal-based automation of smart infrastructures.