Pressure and fatigue information monitoring method for intelligent safety helmet

Abstract

The invention relates to the technical field of biomedical signal processing, in particular to a pressure and fatigue information monitoring method for an intelligent safety helmet. The method comprises the following steps: step S10, collecting an electroencephalogram signal; S20, preprocessing the collected signals, and removing baseline drift, power frequency interference and noise; S30, performing feature extraction on the preprocessed signals in a time domain, a frequency domain and a nonlinear domain; S40, carrying out feature selection by adopting a maximum correlation and minimum redundancy algorithm, and respectively carrying out selection and reservation on related features of fatigue detection and pressure detection; S50, recognizing and training fatigue detection and pressure detection through a BP neural network, constructing a fatigue grade F and a pressure grade P, and establishing a mental state evaluation equation S=eF-1+P, wherein in the equation, S is a mental state index; and step S60, comprehensively evaluating the fatigue grade F, the pressure grade P and the mental state index S of a worker, and judging the mental state of the worker.

Description

technical field [0001] The invention relates to the technical field of biomedical signal processing, in particular to a method for monitoring pressure and fatigue information of an intelligent safety helmet. Background technique [0002] There are some high-risk types of work, such as high-altitude operations in electric power, which require workers to maintain concentration and energy during operations, otherwise safety accidents are likely to occur. However, during high-risk operations, workers are always facing greater mental pressure and are prone to fatigue. However, most people's self-perceived stress and fatigue do not match the actual situation, or they still insist on working even under the condition of self-perceived stress or fatigue. Therefore, it is necessary to monitor stress and fatigue. [0003] EEG signals are an important indicator for evaluating mental state, and can effectively reflect mental stress or fatigue. However, most workers in high-risk operation...

AI Technical Summary

Problems solved by technology

[0002] There are some high-risk types of work, such as high-altitude operations in electric power, which require workers to maintain concentration and energy during operations, otherwise safety accidents are likely to occur. However, during high-risk operations, workers are always facing greater mental pressure and are prone to fatigue

However, most people's self-perceived stress and fatigue do not match the actual situation, or they still insist on working even under the state of self-perceived stress or fatigue, so it is necessary to monitor stress and fatigue

[0003] The EEG signal is an important indicator for evaluating the mental state, which can effectively reflect the state of mental stress or fatigue. However, most of the workers in high-risk operations are only equipped with environmental monitoring devices, which cannot monitor the physiological state of the workers in real time.

Moreover, in the prior art, the research based on EEG monitoring of mental state is only carried out from the perspective of fatigue or stress. In fact, stress and fatigue are closely related. Long-term mental stress is an important factor leading to fatigue. Stress and fatigue Jointly affect the current mental state of workers, and only monitor one of fatigue or stress, which cannot fully reflect the negative mental state

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