Head mounted fatigue detector and method

Abstract

The invention discloses a head mounted fatigue detector and a method. The detector comprises an EEG (Electroencephalogram) collecting module, an EEG analysis module, a fatigue estimator, a voice broadcast module and a power supply module, wherein the EEG collecting module is used for acquiring primary EEG, the EEG analysis module is used for receiving the primary EEG in real time, and processing the primary EEG by a standard time frequency analytical algorithm to obtain characteristic parameters, including an EEG rhythm power spectrum and blinking frequency; the fatigue estimator is used for realizing the estimation on the degree of fatigue of a user in accordance with the distribution of spaces where the characteristic parameters are located, to generate a fatigue estimation result, the voice broadcast module is used for playing the fatigue estimation result to the user in real time, and the power supply module is used for providing energy to all modules. The head mounted fatigue detector provided by the invention is simple in structure, extensive in applicability and excellent in performance, and is capable of solving a fatigue detection problem, thereby having a better market prospect.

Description

technical field [0001] The invention belongs to the field of physiological parameter detection, in particular to a head-mounted fatigue detection device and method. Background technique [0002] With the rapid development of modern transportation industry, traffic accidents have become a serious problem facing the world. According to statistics, the number of deaths caused by traffic accidents in the world reaches 600,000 every year, and the direct economic loss is about 12.5 billion US dollars. Among these accidents, 57% of catastrophic accidents are related to driver fatigue. [0003] In recent years, the problem of fatigue detection has been paid more and more attention by researchers all over the world. At present, the detection methods of fatigue driving are mainly divided into three categories, namely, methods based on behavioral characteristics, methods based on image processing, and methods based on physiological detection. [0004] First of all, the detection meth...

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Problems solved by technology

[0004] First of all, the detection method based on behavioral features is constrained by different driving habits of drivers, and is also interfered by long-term intensive training factors

Secondly, the method based on image processing requires that the user's face must be in a suitable spatial range (out of range, detection cannot be realized), and the camera must be able to capture frontal images (non-frontal face recognition technology currently has huge flaws that cannot be solved in the short term)

Finally, the signals used in the method based on physiological signals mainly include heart rate, respiration, head movement, human eye and mouth shape, etc. The acquisition of these physiological signals is poor in ecology, and it is not very convenient to wear corresponding sensors

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