Autonomous Integrated Sensing and Processing for Bioradar: Advancing Non-Invasive Biomedical Monitoring and Signal Analysis

This paper presents a novel automated system that leverages a Raspberry Pi to operate a 24 GHz single-channel continuous wave (CW) radar for through-the-wall concurrent vital sign monitoring of two subjects. The system autonomously performs independent component analysis using the joint approximation diagonalization of eigenmatrices (ICA-JADE) to separate both individuals' breathing rates (BR). It applies the maximal overlap discrete wavelet transform (MODWT) after ICA-JADE to extract heart rates (HR). By integrating the radar system and ICA-JADE into an embedded platform, the system operates without requiring manual intervention. The automation framework enables real-time processing, enhancing the system's practical deployment in real-world applications. The results demonstrate high accuracy in isolating BR and HR using ICA-JADE, as benchmarked against conventional chest belts and ECG measurements. This fully automated approach shows significant potential for applications such as search and rescue operations, security surveillance, and remote healthcare monitoring through barriers.