CN113967014A - Student behavior analysis device, system and method based on big data - Google Patents
Student behavior analysis device, system and method based on big data Download PDFInfo
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- CN113967014A CN113967014A CN202111581016.2A CN202111581016A CN113967014A CN 113967014 A CN113967014 A CN 113967014A CN 202111581016 A CN202111581016 A CN 202111581016A CN 113967014 A CN113967014 A CN 113967014A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1103—Detecting eye twinkling
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1116—Determining posture transitions
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
Abstract
The invention relates to a student behavior analysis device, a system and a method based on big data, which belong to the technical field of student behavior data analysis, and are characterized in that different progressive detection modes and detection result detection schemes are designed, the image periodic detection with the lowest cost is used as a trigger, the long-time running of video acquisition is avoided, and the detection modes are divided into three degrees of suspicious drowsy state, serious drowsy state and drowsy state in sequence, so that the student behavior analysis result has higher accuracy and better reliability. The method is characterized in that facial images are collected periodically, the cost is extremely low compared with that of video collection, the facial images are used as long-time starting device equipment, and subsequent progressive inspection and detection are triggered after abnormality is detected.
Description
Technical Field
The invention belongs to the technical field of student behavior data analysis, and particularly relates to a student behavior analysis device, a student behavior analysis system and a student behavior analysis method based on big data.
Background
The current student behavior analysis scheme is to detect different states of students in class, including whether the students are dozing and sleeping.
In the prior art, different states of students in class are detected more comprehensively, the mode of carrying out video detection on the students in the whole course of class is adopted violently, the cost problem of video detection equipment facilities is ignored completely, different detection modes and detection result detection schemes which are gradual are not designed, and the accuracy of student behavior analysis results is lower and the reliability is poorer.
Therefore, in the present stage, it is necessary to design a student behavior analysis device, system and method based on big data to solve the above problems.
Disclosure of Invention
The invention aims to provide a student behavior analysis device, a student behavior analysis system and a student behavior analysis method based on big data, which are used for solving the technical problems in the prior art, such as: in the prior art, different states of students in class are detected more comprehensively, the mode of carrying out video detection on the students in the whole course of class is adopted violently, the cost problem of video detection equipment facilities is ignored completely, different detection modes and detection result detection schemes which are gradual are not designed, and the accuracy of student behavior analysis results is lower and the reliability is poorer.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a big-data-based student behavior analysis apparatus comprising:
the eyelid tension detection device is used for detecting whether the eyelid tension of the student is abnormal or not;
blink frequency detection means for detecting whether or not the blink frequency of the student is abnormal;
the nodding speed detection device is used for detecting whether the nodding speed of the student is abnormal or not;
wherein the eyelid tension detection device, the blink frequency detection device and the nodding speed detection device are in a closed state;
when the student behavior analysis device is started, the eyelid tension detection device is started;
if the eyelid tension detection device detects that the eyelid tension of the student is abnormal, the blinking frequency detection device is started, and the student behavior analysis device judges that the student behavior is in a suspected sleepy state;
if the blinking frequency detection device detects that the blinking frequency of the student is abnormal, the nodding speed detection device is started, and the student behavior analysis device judges that the student behavior is in a severe suspected sleepy state;
and if the nodding speed detection device detects that the nodding speed of the student is abnormal, the student behavior analysis device judges that the student behavior is in a doze state.
Further, in the eyelid flare degree detecting apparatus,
collecting facial images of students at regular intervals;
acquiring the eyelid tension of the student according to the facial image;
and matching and comparing the eyelid tension with a preset standard eyelid tension, if the eyelid tension is not matched with the preset standard eyelid tension, judging that the eyelid tension is abnormal at the moment, and if not, judging that the eyelid tension is normal.
Further, the fixed time period is an integral multiple of the eye opening and blinking periods of the students in normal states;
the eye opening and blinking periods are screened from the student's historical data.
Further, in the blink frequency detection apparatus,
collecting facial videos of students in a preset time period;
acquiring the blinking frequency of the student according to the face video;
and matching and comparing the blink frequency with a preset standard blink frequency, if the blink frequency is not matched with the preset standard blink frequency, judging that the blink frequency is abnormal at the moment, and if not, judging that the blink frequency is normal.
Further, in the nodding speed detecting device,
continuously collecting body contour videos of students;
acquiring the nodding speed of the student according to the body contour video;
and matching and comparing the nodding speed with a preset standard nodding speed, judging that the nodding speed is abnormal at the moment if the nodding speed is not matched with the preset standard nodding speed, and otherwise, judging that the nodding speed is normal.
Further, the student computer desk further comprises a gravity center deviation detection device for detecting whether the gravity center deviation of the upper body of the student is abnormal;
if the gravity center shift detection device detects that the gravity center shift of the upper body of the student is abnormal, the student behavior analysis device judges that the student behavior is in a sleeping state.
The student behavior analysis system based on the big data comprises the student behavior analysis device based on the big data and a cloud server, and the student behavior analysis device and the cloud server are in data interaction.
The student behavior analysis method based on the big data adopts the student behavior analysis device based on the big data to analyze the student behavior based on the big data.
A storage medium having stored thereon a computer program which, when executed, performs a big-data based student behavior analysis method as described above.
An electronic device comprises a processor and a memory, wherein the memory is used for storing executable commands of the processor, and the processor executes the executable commands to realize a big data-based student behavior analysis method.
Compared with the prior art, the invention has the beneficial effects that:
one of the beneficial effects of the scheme is that different progressive detection modes and detection result detection schemes are designed, the image periodic detection with the lowest cost is used as triggering, the long-time running of video acquisition is avoided, and the method is divided into three degrees of suspected drowsy state, severe suspected drowsy state and drowsy state in sequence, so that the student behavior analysis result is higher in accuracy and better in reliability. The method is characterized in that facial images are collected periodically, the cost is extremely low compared with that of video collection, the facial images are used as long-time starting device equipment, and subsequent progressive inspection and detection are triggered after abnormality is detected.
Drawings
Fig. 1 is a schematic view of a working principle process of a student behavior analysis device according to an embodiment of the present application.
Fig. 2 is a schematic view of a working principle process of the eyelid flare degree detection apparatus according to the embodiment of the present application.
Fig. 3 is a schematic process diagram of an operation principle of the blink frequency detection apparatus according to the embodiment of the application.
Fig. 4 is a schematic process diagram of the working principle of the nodding speed detection device according to the embodiment of the application.
Fig. 5 is an explanatory diagram of eyelid flare in the embodiment of the present application.
Fig. 6 is a schematic diagram illustrating the self-help body balance maintaining process according to the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 6 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the prior art, different states of students in class are detected more comprehensively, the mode of carrying out video detection on the students in the whole course of class is adopted violently, the cost problem of video detection equipment facilities is ignored completely, different detection modes and detection result detection schemes which are gradual are not designed, and the accuracy of student behavior analysis results is lower and the reliability is poorer.
As shown in fig. 1, a big data-based student behavior analysis apparatus is provided, including:
the eyelid openness detection device is used for detecting whether the eyelid openness of the student (the degree of opening and contraction of the eyelid is larger, namely, the larger the opening degree is, the smaller the eye opening is, the larger the contraction degree is, and the larger the eye opening is, as shown in fig. 5, the schematic diagram is only used as an explanatory explanation, does not limit the protection range of the scheme, and is not the only choice of the scheme) is abnormal;
blink frequency detection means for detecting whether or not the blink frequency of the student is abnormal;
the nodding speed detection device is used for detecting whether the nodding speed of the student is abnormal or not;
wherein the eyelid tension detection device, the blink frequency detection device and the nodding speed detection device are in a closed state;
when the student behavior analysis device is started, the eyelid tension detection device is started;
if the eyelid tension detection device detects that the eyelid tension of the student is abnormal, the blinking frequency detection device is started, and the student behavior analysis device judges that the student behavior is in a suspected sleepy state;
because the difference between the pupil size of a small number of students and the average pupil size is large, misjudgment of the doze state is possible by directly judging, and the doze suspicion is more reasonable to judge.
If the blinking frequency detection device detects that the blinking frequency of the student is abnormal, the nodding speed detection device is started, and the student behavior analysis device judges that the student behavior is in a severe suspected sleepy state;
since the students sometimes classify the blinking frequency of the students into a state matched with the drowsy state because the eyes of the students are not relaxed (for example, the students carelessly focus on the content on the blackboard), the students can judge that the drowsy state is misjudged directly, and the judgment that the serious drowsy is suspected is more reasonable.
And if the nodding speed detection device detects that the nodding speed of the student is abnormal, the student behavior analysis device judges that the student behavior is in a doze state.
When the student sleeps, the head of the student can be noded in a weightless mode, and the nodding speed is extremely special under the condition.
In the scheme, different progressive detection modes and detection result detection schemes are designed, the image periodic detection with the lowest cost is used as triggering, the long-time running of video acquisition is avoided, and the detection schemes are divided into the steps of suspected sleepy state, severe suspected sleepy state and sleepy state in sequence, so that the accuracy of student behavior analysis results is high, and the reliability is good.
Further, as shown in fig. 2, in the eyelid flare degree detecting apparatus,
collecting facial images of students at regular intervals;
acquiring the eyelid tension of the student according to the facial image;
and matching and comparing the eyelid tension with a preset standard eyelid tension, if the eyelid tension is not matched with the preset standard eyelid tension, judging that the eyelid tension is abnormal at the moment, and if not, judging that the eyelid tension is normal.
In the scheme, the face images are collected periodically, the cost is extremely low compared with that of video collection, the face images are used as long-time starting device equipment, and subsequent progressive inspection and detection are triggered after abnormality is detected.
Further, the fixed time period is an integral multiple of the eye opening and blinking periods of the students in normal states;
the eye opening and blinking periods are screened from the student's historical data.
Further, as shown in fig. 3, in the blink frequency detection apparatus,
collecting facial videos of students in a preset time period;
acquiring the blinking frequency of the student according to the face video;
and matching and comparing the blink frequency with a preset standard blink frequency, if the blink frequency is not matched with the preset standard blink frequency, judging that the blink frequency is abnormal at the moment, and if not, judging that the blink frequency is normal.
In the scheme, the face images are collected periodically, and the face video collection of students in the preset time period is triggered after the abnormality is detected, so that the cost is reduced, and meanwhile, the reliability of the detection result is further improved.
Further, as shown in fig. 4, in the nodding-speed detecting device,
continuously collecting body contour videos of students;
acquiring the nodding speed of the student according to the body contour video;
and matching and comparing the nodding speed with a preset standard nodding speed, judging that the nodding speed is abnormal at the moment if the nodding speed is not matched with the preset standard nodding speed, and otherwise, judging that the nodding speed is normal.
In the above scheme, the face video of the student is collected within the preset time period, and the body contour video of the student is triggered to be collected continuously after the abnormality is detected, so that the cost is reduced, and meanwhile, the reliability of the detection result is further improved.
Further, the student computer desk further comprises a gravity center deviation detection device for detecting whether the gravity center deviation of the upper body of the student is abnormal;
if the gravity center shift detection device detects that the gravity center shift of the upper body of the student is abnormal, the student behavior analysis device judges that the student behavior is in a sleeping state.
In the scheme, the gravity center deviation detection device is also designed to be started periodically, and the gravity center deviation condition of the upper half of the student is judged through image acquisition, identification and judgment, and whether the gravity center deviation is abnormal or not is judged according to the physical and mechanical balance of the upper half of the student (as shown in fig. 6, the schematic diagram is only used as an explanatory explanation and does not limit the protection range of the scheme, but is not the only choice of the scheme because the sitting posture of the student is self-acting to maintain the physical balance when the student is in a waking state, but the sitting posture of the student is supported by the outside of a seat or a desk to maintain the physical balance).
The student behavior analysis system based on the big data comprises the student behavior analysis device based on the big data and a cloud server, and the student behavior analysis device and the cloud server are in data interaction.
The student behavior analysis method based on the big data adopts the student behavior analysis device based on the big data to analyze the student behavior based on the big data.
A storage medium having stored thereon a computer program which, when executed, performs a big-data based student behavior analysis method as described above.
An electronic device comprises a processor and a memory, wherein the memory is used for storing executable commands of the processor, and the processor executes the executable commands to realize a big data-based student behavior analysis method.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (8)
1. A student behavior analysis device based on big data, comprising:
the eyelid tension detection device is used for detecting whether the eyelid tension of the student is abnormal or not;
blink frequency detection means for detecting whether or not the blink frequency of the student is abnormal;
the nodding speed detection device is used for detecting whether the nodding speed of the student is abnormal or not;
wherein the eyelid tension detection device, the blink frequency detection device and the nodding speed detection device are in a closed state;
when the student behavior analysis device is started, the eyelid tension detection device is started;
if the eyelid tension detection device detects that the eyelid tension of the student is abnormal, the blinking frequency detection device is started, and the student behavior analysis device judges that the student behavior is in a suspected sleepy state;
if the blinking frequency detection device detects that the blinking frequency of the student is abnormal, the nodding speed detection device is started, and the student behavior analysis device judges that the student behavior is in a severe suspected sleepy state;
and if the nodding speed detection device detects that the nodding speed of the student is abnormal, the student behavior analysis device judges that the student behavior is in a doze state.
2. The big-data-based student behavior analysis device according to claim 1, wherein in the eyelid tension detection device,
collecting facial images of students at regular intervals;
acquiring the eyelid tension of the student according to the facial image;
and matching and comparing the eyelid tension with a preset standard eyelid tension, if the eyelid tension is not matched with the preset standard eyelid tension, judging that the eyelid tension is abnormal at the moment, and if not, judging that the eyelid tension is normal.
3. The big-data based student behavior analysis device according to claim 2, wherein the fixed time period is an integer multiple of a normal eye-opening and eye-blinking period of the student;
the eye opening and blinking periods are screened from the student's historical data.
4. The big-data based student behavior analysis device of claim 2, wherein the blink frequency detection device,
collecting facial videos of students in a preset time period;
acquiring the blinking frequency of the student according to the face video;
and matching and comparing the blink frequency with a preset standard blink frequency, if the blink frequency is not matched with the preset standard blink frequency, judging that the blink frequency is abnormal at the moment, and if not, judging that the blink frequency is normal.
5. The big-data-based student behavior analysis device according to claim 4, wherein in the nodding-speed detecting device,
continuously collecting body contour videos of students;
acquiring the nodding speed of the student according to the body contour video;
and matching and comparing the nodding speed with a preset standard nodding speed, judging that the nodding speed is abnormal at the moment if the nodding speed is not matched with the preset standard nodding speed, and otherwise, judging that the nodding speed is normal.
6. The big-data-based student behavior analysis apparatus according to claim 1, further comprising a center-of-gravity shift detection means for detecting whether or not a center-of-gravity shift of the upper body of the student is abnormal;
if the gravity center shift detection device detects that the gravity center shift of the upper body of the student is abnormal, the student behavior analysis device judges that the student behavior is in a sleeping state.
7. A student behavior analysis system based on big data, comprising the student behavior analysis device based on big data as claimed in any one of claims 1 to 6, further comprising a cloud server, wherein the student behavior analysis device performs data interaction with the cloud server.
8. A student behavior analysis method based on big data, characterized in that the student behavior analysis based on big data is carried out by using the student behavior analysis device based on big data according to any one of claims 1 to 6.
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