Radar-based sleep characteristic event detection method, device and storage medium

Abstract

The invention discloses a radar-based sleep characteristic event detection method, device and a storage medium. The method comprises the steps as follows: S10, transmitting waveform to a sleeper in adetection range of radar and receiving a target echo signal by radar, establishing an echo signal model; and S20, preprocessing the received echo signal to obtain a restrained echo signal. The radar-based sleep characteristic event detection method fits distribution of power burst curves of sleep events and sleep characteristic events by matlab by analyzing power burst curve distribution of the sleep events and sleep characteristic events, judges whether distribution of P(Z|H1) and P(Z|H10) obeys lognormal normal distribution, and then detects numerical values of power burst curves by an adaptive logarithm double-threshold constant false-alarm detector; and the technical scheme can determine that power burst curve characteristics conform to normal distribution after taking logarithm valuesand accordingly, designs the adaptive logarithm double-threshold constant false-alarm detector to detect sleep characteristic events.

Description

technical field [0001] The present invention relates to the technical field of sleep monitoring, in particular to a radar-based sleep characteristic event detection method, device and storage medium. Background technique [0002] As people pay more attention to health issues, more and more medical testing equipment are more miniaturized and portable. However, the most important sleep quality monitoring equipment is mainly dominated by the use of photoplethysmography (Photoplethysmography) medical technology; according to medical research, the quality of human sleep is related to the degree of human health, and the quality of sleep is related to the degree of human health during sleep. The number of characteristic events (turning over, getting out of bed and going to bed after leaving the bed) has a certain relationship, so how to detect the characteristic events of human sleep has become a more important thing in sleep monitoring. [0003] Nowadays, the devices that can mon...

AI Technical Summary

Problems solved by technology

Among them, when using a camera as a sleep monitoring sensor, its computational complexity is high, and it is easy to violate personal privacy; the non-contact capacitive sensor is not effective when it is 50mm away from the human body; and the ultrasonic sensor is easily affected by the environment, and there is a triangle error. Specular reflection errors and multiple reflection errors leading to performance degradation

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