Wearable AI System for Real Time Threat Detection and Dual Spectrum Analysis

Abstract

The development of autonomous wearable perception devices with real-time environmental awareness has been sped up by the convergence of robotics, embedded systems, and artificial intelligence (AI). However, the majority of current assistive and surveillance systems are still limited by their static deployment, reliance on networks, and subpar performance in low-visibility scenarios. The theoretical underpinnings and literature review of a wearable artificial intelligence system for dual-spectrum analysis and real-time threat detection are presented in this research. Mounted on a small robotic arm platform, the device incorporates a dual-camera vision module that combines an RGB sensor for daylight and an infrared night-vision sensor for darkness. Optimized lightweight deep-learning models converted to TensorFlow Lite are used to handle the visual data fully on edge devices like the Google Coral Dev Board or Raspberry Pi 4. Users without cloud access can receive instant spatial alerts thanks to the design's ability to detect objects on-device and provide multimodal feedback (tactile and audio). Defense surveillance for autonomous threat identification and assistive navigation for visually impaired people are the two applications that the framework is intended for. This paper highlights research gaps in wearable edge-AI systems for adaptive multimodal perception by concentrating on the theoretical underpinnings and literature synthesis supporting the development.