FMCW radar based high-precision gridless non-contact vital signs monitoring for Internet of Medical Things.

BACKGROUND

Vital signs monitoring is of paramount importance in healthcare, serving as a crucial component in disease prevention, diagnosis, and management. Traditional contact-based devices, including electrocardiographs and pulse oximeters, while providing vital data, face limitations in long-term use owing to patient discomfort.

OBJECTIVE

This study aims to propose a non-contact monitoring system utilizing Frequency Modulated Continuous Wave (FMCW) radar for continuous, non-invasive health monitoring. The objective is to overcome the constraints of traditional methods and enhance the feasibility of remote chronic disease management.

METHODS

The proposed system employs the Multiple Signal Classification (MUSIC) algorithm to estimate respiration and heart rates. To tackle challenges such as noise interference and signal overlap, an enhanced root-MUSIC algorithm is introduced. This algorithm transforms the single-channel model into a multi-channel one and optimizes signal estimation through semi-definite programming (SDP) and the Alternating Direction Method of Multipliers (ADMM). Simulations and real-world experiments were conducted to validate the system's effectiveness.

RESULTS

The validation process demonstrated the system's efficacy, revealing that the multi-channel model significantly reduces theoretical error bounds. In experimental trials, the method achieved a respiration rate Root Mean Squared Error (RMSE) of 0.0131 Hz and a heart rate RMSE of 0.0394 Hz, with corresponding accuracies of 96.05% and 90%. Bland-Altman analysis further corroborated strong concordance with contact-based devices.