Electric bed control method and system based on deep learning algorithm and computer program

Abstract

The invention discloses an electric bed control method based on a deep learning algorithm. The electric bed control method comprises the following steps: receiving snore data corresponding to snore monitored in real time from intelligent equipment after an intervention judgment period is started; receiving body movement frequency monitoring data from an electric bed controller; judging whether the body movement frequency monitoring data is greater than a preset threshold value or not; and if the body movement frequency monitoring data is greater than the preset threshold value, setting parameters on the basis of one of multiple intervention levels received from the intelligent equipment and an intervention mode in the intervention period according to the number of times of snore data received in the intervention judgment period so as to drive at least one part of an electric bed to act so as to intervene generation of the snore. The invention also discloses a corresponding control system and a computer program. According to the method and the system, a snoring intervention process based on mobile phone APP is provided, and snoring grade judgment and corresponding adjustment can be realized by adjusting the threshold value within a monitoring period based on threshold value setting of snoring intervention of the mobile phone APP.

Description

technical field [0001] The present invention relates to a control method and system for an electric bed, in particular to a method and system for monitoring snoring based on a deep learning algorithm and controlling an electric bed. Background technique [0002] According to incomplete statistics, nearly 20% of the population in my country snores, and those who snore severely may even cause obstructive apnea, which seriously affects their health. [0003] No. CN103251388A Chinese patent application discloses a method for monitoring and preventing snoring. The method classifies the collected audio signals into severe snoring signals and mild snoring signals and trains severe and mild GMM templates, which are collected by mobile phones The snoring signal, after extracting features, is brought into the GMM template for classification. If it is judged to be severe snoring, the snoring interval is judged, and an external stimulus is taken to intervene in snoring. [0004] No. CN...

AI Technical Summary

Problems solved by technology

[0007] The present invention provides an electric bed control method based on deep learning and voice recognition technology to solve the technical problems that the applicant has identified in the prior art, such as the inability to obtain user feedback and possible interference with user sleep.

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