Contact-Free Cardiovascular Monitoring Using AI-Driven Radar and Sensor Fusion on a Hybrid Edge-Cloud Platform

Access to essential cardiovascular parameters such as heart rate (HR), heart rate variability (HRV), and blood pressure (BP) remains limited in low-income and remote populations, particularly among older adults in developing regions. Continuous, simultaneous, and contact-free monitoring of these parameters beyond close proximity can enhance early detection, screening, and management of cardiovascular and related conditions. This study presents a real-time, contact-free health monitoring system based on millimeter-wave (mmWave) FMCW radar, phase demodulation, and digital signal processing (DSP), integrated with multimodal sensor fusion and artificial intelligence (AI)-driven inference. Sub-millimeter chest wall displacements are captured using radar in-phase and quadrature (I/Q) signals to extract beat-to-beat physiological features, including ECG-correlated waveform components, HR, and HRV, while non-invasive blood pressure is indirectly estimated using a physics-informed adaptive learning framework. A custom Long Short-Term Memory (LSTM) neural network is employed for temporal smoothing and stabilization of HRV signals, improving robustness under real-world conditions. The system is implemented within a hybrid edge–cloud architecture, enabling on-device inference for real-time monitoring and cloud-based analytics for long-term analysis and integration. Clinical-like validation conducted on over 100 adult participants demonstrates measurement accuracy comparable to clinically accepted reference devices, and statistical analysis confirms the robustness and reliability of the proposed system.

@article{Shetty2025,
  title     = {Contact-Free Cardiovascular Monitoring Using AI-Driven Radar and Sensor Fusion on a Hybrid Edge-Cloud Platform},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {K Ravindra Shetty and Shanthala K V and Nishanth A R and Himani Jain},
  doi       = {10.14569/IJACSA.2025.0161261},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161261}
}