An Intelligent Speech Recognition Wrist Sleep Apnea Monitoring System

Abstract

The invention discloses an intelligent voice recognition wrist sleep apnea monitoring system, which includes: a normal snoring sound recording module, a snoring sound database, a snoring sound accurate collection module, a sleep breathing monitoring module, an audio feature recognition module and an apnea early warning module. The technology retrieves the user's normal snoring audio, identifies the snoring audio, and matches the currently monitored snoring audio with it. When it is found that the current audio does not have a matching historical normal audio, an early warning signal is sent through the apnea early warning module. The accurate snoring sound acquisition module adjusts the angle of receiving sound and fixes the range of receiving sound according to the position of sound source and wrist position, which reduces the noise interference of the surrounding environment, improves the accuracy of snoring sound analysis results, and helps to check whether the user has sleep breathing as soon as possible. Pause the symptoms and treat them as early as possible, while also allowing the user to wake up from sleep and avoid physical trauma.

Description

technical field [0001] The invention relates to the technical field of speech recognition, in particular to an intelligent speech recognition wrist sleep apnea monitoring system. Background technique [0002] Sleep apnea refers to a sleep disorder in which breathing stops during sleep, accompanied by sleep defects, fatigue, arrhythmia and other states. The main factor leading to sleep apnea is upper airway obstruction. The clinical manifestation is snoring, although snoring may not It indicates that the user has symptoms of sleep apnea. However, if the user snores during sleep, and has enough sleep time but is tired, there may be serious health problems. Therefore, it is necessary to monitor and manage the user's sleep apnea in order to detect abnormalities in time and processing to ensure the sleep health of the user. Since snoring is a clinical manifestation of sleep apnea, the recognition of snoring is the best way to reflect whether the user suffers from sleep apnea symp...

AI Technical Summary

Problems solved by technology

[0002] Sleep apnea refers to a sleep disorder in which breathing stops during sleep, accompanied by sleep defects, fatigue, arrhythmia and other states. The main factor leading to sleep apnea is upper airway obstruction. The clinical manifestation is snoring, although snoring may not It indicates that the user has symptoms of sleep apnea. However, if the user snores during sleep, and has enough sleep time but is tired, there may be serious health problems. Therefore, it is necessary to monitor and manage the user's sleep apnea in order to detect abnormalities in time and processing to ensure the sleep health of the user. Since snoring is a clinical manifestation of sleep apnea, the recognition of snoring is the best way to reflect whether the user suffers from sleep apnea symptoms. The traditional method of recognizing snoring needs to connect a large number of physiological electrodes on the user , it often causes the user to be unable to fall asleep naturally due to discomfort, and noise interference needs to be reduced in the process of recognizing snoring, so that the sound collection device can accurately receive the user's voice audio, which reduces the difficulty and improves the efficiency of subsequent snoring analysis. Accuracy of Analysis Results

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