Nighttime learning method based on multiple stimuli

A learning method and multi-stimulation technology, applied in the field of learning tools, can solve the problems of resistance, easy sleepiness, and students' discomfort, and achieve the effect of improving the detection speed, reducing the search area, and improving the detection efficiency.

Pending Publication Date: 2021-08-24
ZHENGZHOU RAILWAY VOCATIONAL & TECH COLLEGE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, limited by the equipment required for physiological signal extraction and analysis, physiological signals cannot directly reflect the degree of fatigue, and it is difficult to realize real-time monitoring. Secondly, most of the currently widely used sensor equipment for physiological signal detection is contact and needs to be tested People wear some devices, such as bracelets, wires, electrodes, etc., which are invasive tests. In the process of learning English, most people are resistant to English, and English needs to memorize too much content, and it takes a lot of time to learn...

Method used

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  • Nighttime learning method based on multiple stimuli
  • Nighttime learning method based on multiple stimuli
  • Nighttime learning method based on multiple stimuli

Examples

Experimental program
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Embodiment 1

[0082] combine figure 1 and figure 2 , a nighttime learning method based on multiple stimuli, including a learning system, the learning system includes an image acquisition unit for monitoring the students and an intervention unit for intervening on the fatigue of the students, and the image acquisition unit and the intervention unit are connected by communication to the main control unit;

[0083] The image acquisition unit includes a face acquisition module, an eye acquisition module and a mouth acquisition module;

[0084] The intervention unit includes a spray module, a blue light module and a sound module;

[0085] The main control unit includes an analysis module and a control module; the control module judges the fatigue degree of the trainee according to the analysis result of the analysis module, and the control module sends a signal to the intervention unit according to the result of the judgment, and through the intervention The unit wakes up the students;

[...

Embodiment 2

[0100] On the basis of Embodiment 1, when the analysis module analyzes the information collected by the face acquisition module, on the basis of the Rein-Lien Hsu algorithm, it analyzes the skin color distribution of the face in a single image, according to The skin color distribution characteristics of each image are used to segment the area, automatically select similar areas as the skin color area, detect and locate the face, directly locating the human eye in the image often causes a relatively large error, and use the improved skin color detection algorithm to locate the person Face, first locate the face part, which can reduce the search range of the eyes, improve the detection speed of the eyes, introduce the skin color feature into the face detection algorithm, use the skin color feature to eliminate the influence of non-skin color as much as possible, and segment possible candidate faces area, in order to reduce the scope of the search eye, improve the efficiency of th...

Embodiment 3

[0108] On the basis of embodiment 2, when the analysis module analyzes the information collected by the face acquisition module, on the basis of the Rein-Lien Hsu algorithm, it analyzes the skin color distribution of the face in a single image, according to The skin color distribution characteristics of each image are used for regional segmentation, and similar areas are automatically selected as skin color areas to detect and locate human faces.

[0109] The detection algorithm steps of described human face skin color are as follows:

[0110] S1. Set the collected image format to YUV (4:2:2:), directly take the logarithm of the UV value of each pixel to form a two-dimensional histogram showing a certain peak shape, and introduce the parameter K to eliminate interference , and there is K=S / H, where S represents the shortest distance from the peak to the adjacent peak valley, and H represents the height of the peak;

[0111] S2. Detect each peak area in the two-dimensional his...

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Abstract

The invention relates to a nighttime learning method based on multiple stimuli, and the method comprises the steps: 1) an analysis module analyzes the data of an image collection unit, and continuously carries out image collection of a student when the data is judged to have a fatigue symptom and an interference unit is started, and the data is judged to have no fatigue symptom; 2) a blue light module is started, if the trainee is sober, an interference unit is canceled, and if the trainee does not respond, it is judged that the trainee is still in a fatigue state; 3) a spraying module is starated, if the trainee is sober, cancelling an interference unit, and if the trainee does not make any response, whether the trainee is in a fatigue state is judged; 4) a sound module of the interference unit is started, if the trainee is sober, the work of the interference unit is canceled and if the trainee does not make any response, the trainee is still in a fatigue state; 5) the sound module continues to work, and decibel is increased until the trainee responds. According to the nighttime learning method provided by the invention, through evaluation of eye and mouth states, early warning is carried out according to a human body fatigue level, a person is prompted to be sober from fatigue, and alertness is improved.

Description

technical field [0001] The invention belongs to the technical field of learning appliances, and in particular relates to a night learning method based on multiple stimuli. Background technique [0002] Heart rate, respiratory rate, and lumbar muscle surface EMG are effective in evaluating fatigue status. Through the study on the correlation between sound and fatigue, it shows that sound intensity, rhythm, and frequency have different influences on fatigue. Physiological signals are the most authentic signals of the human body. Index data can most accurately reflect people's actual mental state in terms of fatigue detection. Therefore, learning fatigue detection technology based on physiological signals has better expressive power in reflecting fatigue state. [0003] However, limited by the equipment required for physiological signal extraction and analysis, physiological signals cannot directly reflect the degree of fatigue, and it is difficult to realize real-time monitori...

Claims

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Application Information

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IPC IPC(8): G06K9/00F16M11/04F16M11/20F16M11/28F16M13/02G08B21/06
CPCF16M13/022F16M11/28F16M11/2007F16M11/04G08B21/06G06V40/166G06V40/168G06V40/193
Inventor 胡文莉杨向格尚季玲梁超慧尚宇许卫红刘博
Owner ZHENGZHOU RAILWAY VOCATIONAL & TECH COLLEGE
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