Movement trend term elimination algorithm based on non-contact type vital sign monitoring system

Abstract

The invention provides a signal processing algorithm of a non-contact type vital sign monitoring system. The algorithm includes the following steps of firstly, sending two routes of orthorhombic heartbeat and breathe digital mixed signals, namely, the route-I signal and the route-Q signal, through the non-contact type vital sign monitoring system, and conducting arc tangent processing on the route-I signal and the route-Q signal to obtain orthorhombic demodulation signals; secondly, conducting waveform reconfiguration on the orthorhombic demodulation signals obtained in the first step through the waveform reconfiguration algorithm based on polynomial curve fitting so as to obtain movement trend term information waveforms in the orthorhombic demodulation signals; thirdly, filtering the movement trend term information waveforms obtained in the second step out of the orthorhombic demodulation signals obtained in the first step so as to obtain pre-processed orthorhombic demodulation signals. The trend term information is obtained by conducting fitting through the waveform reconfiguration algorithm based on polynomial curve fitting, and the interference caused by the trend term information can be effectively removed after the trend term information is filtered out of echo signals.

Description

technical field [0001] The invention belongs to the field of radar, in particular to a movement trend item elimination algorithm based on a non-contact vital sign monitoring system. Background technique [0002] Heartbeat and breathing are important physical characteristics of the human body. Theoretically, the respiration and heartbeat signals can be separated and extracted accurately by adopting the waveform reconstruction algorithm based on interpolation operation. However, in the actual measurement process, the involuntary small-scale motion information (referred to as motion trend item) of the measured target's body will interfere with the measurement results. The intuitive solution is to use a high-pass filter to filter out, but the design of this filter: (1) requires a very high order, (2) because the cut-off frequency of the trend item cannot be accurately estimated, the reliability is not good. [0003] In many application fields, people often need to obtain data ...

AI Technical Summary

Problems solved by technology

However, in the actual measurement process, the involuntary small-scale motion information of the measured target body (referred to as the motion trend item) will interfere with the measurement results

The intuitive solution is to use a high-pass filter to filter out, but the design of this filter: (1) requires a very high order, (2) because the cut-off frequency of the trend item cannot be accurately estimated, the reliability is not good

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