A method and system for detecting eye muscle fatigue

Abstract

The invention discloses an eye muscle fatigue detection method and a detection system; the method and system can detect eye ciliary muscle myoelectricity so as to determine eye fatigue conditions; the method comprises the following steps: collecting human body myoelectricity signals; using wavelet and adaptive filtering methods to filter the signals; using a blind source separation method to separate filtered mixed myoelectricity signals carrying other myoelectricity information; using an artificial nerve network and a support vector machine (SVM) to identify separated signals, thus identifying ciliary muscle myoelectricity signals; carrying out time domain frequency domain transition for the ciliary muscle myoelectricity signals; using the artificial nerve network and the support vector machine (SVM) to identify transited ciliary muscle myoelectricity signals, thus identifying eye ciliary muscle status, and providing eye care suggestions for users.

Description

technical field [0001] The invention relates to the field of methods for detecting eye muscle fatigue, in particular to a detection system for detecting eye muscle fatigue. The detection method and related system of the present invention can detect the fatigue degree of the user's eye muscles in real time, and timely provide suggestions for eye use and other medical detection according to the user's glasses fatigue degree. Background technique [0002] 1. According to the Ministry of Education’s survey on the physical health of students across the country, the visual acuity of Chinese students is not optimistic. The poor vision rates of primary school students aged 7-12, junior high school students aged 13-15 and high school students aged 16-18 are as high as 40.89%, 67.33% and 79.20%. According to a report from the Youth Myopia Prevention and Control Center of a municipality directly under the Central Government, the current incidence of myopia is high among 9 to 13 years ...

AI Technical Summary

Problems solved by technology

[0010] Aiming at the problems existing in the prior art, the technical problem that the present invention aims to realize is: acquire the electromyographic signal of the human body surface around the eyes, amplify, filter and separate the electromyographic signal, and identify the ciliary muscle with the method of artificial intelligence. The myoelectric signal of the ciliary muscle is analyzed to analyze the state of the ciliary muscle myoelectricity, that is, the degree of fatigue of the ciliary muscle

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