Dynamic Electromagnetic Model to Detect Human Vital Signs Based on Time-Domain Finite Integration Theorem

Contactless human vital-sign sensing using electromagnetic (EM) waves has made significant progress over the past few years and been practically applicable to a variety of fields such as smart home and healthcare. However, the further development of this technology is hindered by factors such as large volumes of data, long observation periods, and data variability. To deal with this challenge, a dynamic human EM model composed of customized time-varying EM materials is proposed to simulate the periodic characteristics of human cardiopulmonary motions and obtain physiological signals caused by the movements. The EM problem is subsequently addressed by employing the Time-Domain Finite Integration Technique (TDFIT), so that the EM scattering properties associated with human cardiopulmonary movements can be accurately analyzed. To validate the effectiveness of the proposed human EM model, we process the simulated human physiological signals for respiration and heartbeat rate estimation, with the error less than 4% and 8%, respectively. Furthermore, measured experiments are conducted to collected actual human vital-sign signals for comparison. Good agreement between the measured and simulated results demonstrates that the proposed human EM model is capable of accurately simulating the periodic cardiopulmonary motions and thus providing simulated physiological measurements for preliminary validation of vital sign sensing algorithms.