Multimodal Intelligence for Safety-Critical Environments: Agentic AI Fusion of CSI Signals and Visual Perception

Human activity recognition (HAR) is an important facility in safety-critical systems, which include smart homes, healthcare facilities, and surveillance systems. Even though Wi-Fi Channel State Information (CSI)-based HAR has demonstrated itself as a low-cost, device-free, and privacy-sensitive alternative to vision- and wearable-based solutions, the current systems have drawbacks in the form of manually adjusted detection thresholds, low robustness, and lack of flexibility to adapt to changing environments. Parallel to it, vision-based perception is rich in semantic context but is vulnerable to illumination, occlusion and privacy issues. In managing these issues, to provides an agentic multimodal intelligence system that combines CSI messages and computer vision into an autonomous workflow in AI to take the right action in recognizing human activities and preventively averting crisis situations. This proposal uses adaptive CSIbased division of activity, collective learning to classify robustly and use agentic choice to coordinate the multimodal perception in edge settings with resource limitations. Experimental tests in commodity Wi-Fi devices indicate that the extraction accuracy of the system is high both in the indoor and outdoor settings, and multimodal fusion has a significantly greater recognition robustness than the single-modality systems. The framework is to be supported with edge deployment via lightweight frameworks and knowledge-efficient learning plans supporting scalable and privacy-conscientious intelligence. The suggested solution demonstrates the capability of agentic multimodal AI to make further development of reliable HAR and crisisconscious monitoring the next step towards safety-critical applications of the next generation.