CN107374652B - Quality monitoring method, device and system based on electronic product learning - Google Patents
Quality monitoring method, device and system based on electronic product learning Download PDFInfo
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- CN107374652B CN107374652B CN201710596818.8A CN201710596818A CN107374652B CN 107374652 B CN107374652 B CN 107374652B CN 201710596818 A CN201710596818 A CN 201710596818A CN 107374652 B CN107374652 B CN 107374652B
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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
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/168—Evaluating attention deficit, hyperactivity
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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
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/06—Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
- A61M2021/0027—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the hearing sense
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
- A61M2021/0044—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the sight sense
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
- A61M2021/0083—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus especially for waking up
Abstract
The invention discloses a quality monitoring method, a quality monitoring device and a quality monitoring system based on electronic product learning. Wherein, the method comprises the following steps: acquiring brain wave information and eye activity information of a user in the process that the user watches a learning file through an electronic product; integrating and analyzing the brain wave information and the eye activity information to determine the real-time learning state of the user; and marking the real-time learning state at the corresponding playing position in the learning file. Therefore, the real-time learning state of the user is determined to be marked at the corresponding playing position in the learning file by integrating and analyzing the brain wave information and the eye activity information of the user, so that the learning state of the user can be monitored and remarked, the user can conveniently learn and review subsequently, and the learning quality of the user is improved.
Description
Technical Field
The invention relates to the technical field of information technology, in particular to a quality monitoring method, a quality monitoring device and a quality monitoring system based on electronic product learning.
Background
With the advent of the information age and the increasing popularity of networks, more and more users are beginning to learn using electronic products such as computers. For example, learning the required knowledge through video.
However, the lack of supervision and the lower presence of the video lesson itself causes the learner to miss the learning content frequently because of the distraction or drowsiness, or because of the interruption of the middle.
Disclosure of Invention
The object of the present invention is to solve the above technical problem at least to a certain extent.
Therefore, the invention provides a quality monitoring method, a quality monitoring device and a monitoring system based on electronic product learning, which can solve the problem that the learning quality is reduced because a user is distracted or sleepy or the user misses learning content because the user is interrupted in the learning process based on the electronic product in the prior art.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a quality monitoring method based on electronic product learning, including: acquiring brain wave information and eye movement information of the user; performing integration analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user; and marking the real-time learning state at a corresponding playing position in the learning file.
According to the quality monitoring method based on electronic product learning, disclosed by the embodiment of the invention, the real-time learning state of the user is determined to be marked at the corresponding playing position in the learning file by integrating and analyzing the brain wave information and the eye activity information of the user, so that the learning state of the user can be monitored and remarked, the user can conveniently learn and review subsequently, and the learning quality of the user is improved.
In addition, the quality monitoring method based on the electronic product learning of the embodiment of the invention also has the following additional technical characteristics:
optionally, the method further includes: detecting brain wave information of the user through a head-mounted brain wave detection device, and detecting eye activity information of the user through a camera.
Optionally, the performing integrated analysis on the brain wave information and the eye activity information to determine a real-time learning state of the user includes: analyzing the real-time waveband index values of the brain wave information, performing difference analysis on each index value, extracting indexes with differences reaching preset fatigue states, and determining the real-time brain fatigue condition of the user; analyzing the real-time sight signal of the eye activity information, and determining the real-time sight deviation condition or eye fatigue condition of the user; and determining the real-time learning state of the user according to the real-time brain fatigue condition, sight line deviation condition or eye fatigue condition of the user.
Optionally, the step of performing an integrated analysis on the brain wave information and the eye movement information to determine a real-time learning state of the user includes: and performing integrated analysis on the brain wave information and the eye activity information to determine that the real-time learning state of the user is an abnormal learning state.
Optionally, the step of performing an integrated analysis on the brain wave information and the eye activity information to determine that the real-time learning state of the user is an abnormal learning state includes: if the eye closing time of the user is larger than a preset first time and smaller than a preset second time according to the eye activity information analysis, the number of times of the eye closing time is larger than a preset threshold value, and the user is in a drowsy state according to the brain wave information analysis of the corresponding time period, determining that the user is in a doze state currently; if the eye closing time of the user is larger than a preset second time according to the eye activity information analysis, and the user is in a drowsy state according to the brain wave information analysis of the corresponding time period, determining that the user is in a falling asleep state currently; and if the fact that the sight signals of the user deviate from the learning file is analyzed and obtained according to the eye movement information, and the deviation time exceeds the preset time, determining that the user is in the sight deviation state at present.
Optionally, the step of performing an integrated analysis on the brain wave information and the eye activity information to determine that the real-time learning state of the user is an abnormal learning state includes: if the eye movement information is analyzed and obtained that the sight signals of the user do not deviate from the learning file, whether the number of the blink signals of the user meets a preset threshold value or not is detected; when the number of the blink signals reaches a preset threshold value, calculating a variance value of the currently stored blink signal data, and comparing the variance value with a preset variance threshold value; and acquiring the current blinking fatigue of the user according to the comparison result, analyzing and acquiring the drowsiness state of the user according to the brain wave information at the corresponding moment, and determining the drowsiness state of the user.
Optionally, the learning state comprises an abnormal learning state, the method further comprising: and if the real-time learning state of the user is abnormal learning, reminding the user to concentrate on the mental learning through sound and/or light.
Optionally, the learning state comprises an abnormal learning state, the method further comprising: and prompting the user to relearn the file content which is marked in the learning file and corresponds to the abnormal learning state.
In order to achieve the above object, a second aspect of the present invention provides a quality monitoring device based on electronic product learning, including: the acquisition module is used for acquiring brain wave information and eye activity information of the user; the analysis module is used for performing integrated analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user; and the marking module is used for marking the real-time learning state at the corresponding playing position in the learning file.
According to the quality monitoring device based on electronic product learning, disclosed by the embodiment of the invention, the real-time learning state of the user is determined to be marked at the corresponding playing position in the learning file by integrating and analyzing the brain wave information and the eye activity information of the user, so that the learning state of the user can be monitored and remarked, the user can conveniently learn and review subsequently, and the learning quality of the user is improved.
In order to achieve the above object, a third aspect of the present invention provides a quality monitoring system based on electronic product learning, including: brain wave detection device, eye movement detection device, and quality monitoring device based on electronic product learning.
According to the quality monitoring system based on electronic product learning, disclosed by the embodiment of the invention, the real-time learning state of the user is determined to be marked at the corresponding playing position in the learning file by integrating and analyzing the brain wave information and the eye activity information of the user, so that the learning state of the user can be monitored and remarked, the user can conveniently learn and review subsequently, and the learning quality of the user is improved.
In order to achieve the above object, a fourth aspect of the present invention provides another quality monitoring device based on electronic product learning, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: acquiring brain wave information and eye activity information of a user in the process that the user watches a learning file through an electronic product; performing integration analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user; and marking the real-time learning state at a corresponding playing position in the learning file.
In order to achieve the above object, a fifth embodiment of the present invention proposes a non-transitory computer-readable storage medium, wherein instructions, when executed by a processor of a mobile terminal, enable the mobile terminal to execute a quality monitoring method based on electronic product learning, the method comprising: acquiring brain wave information and eye activity information of a user in the process that the user watches a learning file through an electronic product; performing integration analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user; and marking the real-time learning state at a corresponding playing position in the learning file.
In order to achieve the above object, a sixth aspect of the present invention provides a computer program product, wherein when being executed by an instruction processor, the computer program product executes a quality monitoring method based on electronic product learning, and the method comprises: acquiring brain wave information and eye activity information of a user in the process that the user watches a learning file through an electronic product; performing integration analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user; and marking the real-time learning state at a corresponding playing position in the learning file.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow diagram of a method for quality monitoring based on electronic product learning, according to one embodiment of the present invention;
FIG. 2 is a flow diagram of a method of determining a learning state of a user in real-time according to one embodiment of the invention;
FIG. 3 is a schematic diagram of a quality monitoring device based on electronic product learning according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a quality monitoring system based on electronic product learning, according to an embodiment of the present invention;
fig. 5 is a schematic structural view of a brain wave detecting apparatus according to an embodiment of the present invention;
fig. 6 is a schematic configuration diagram of an information processing apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a quality monitoring system based on electronic product learning according to another embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a quality monitoring method, device and system based on electronic product learning according to an embodiment of the present invention with reference to the accompanying drawings.
At present, in order to facilitate learning, more and more users select knowledge required for learning through electronic products. However, this method is easily disturbed by the outside, and the user is easily distracted due to lack of participation sense, and the learning quality is reduced.
In order to solve the above problems, embodiments of the present invention provide a quality monitoring method based on electronic product learning, which determines a real-time learning state of a user to be marked at a corresponding playing position in a learning file by performing integrated analysis on brain wave information and eye activity information of the user, so as to monitor and remark the learning state of the user, facilitate subsequent learning and review of the user, and improve the learning quality of the user. The method comprises the following specific steps:
fig. 1 is a flow chart of a quality monitoring method based on electronic product learning according to an embodiment of the present invention.
As shown in fig. 1, the quality monitoring method based on electronic product learning includes:
Specifically, the user can select to watch the learning file through the electronic product according to the actual application requirement. The electronic product can be a computer, an iPad, a mobile phone and other devices with learning file watching function.
It will be appreciated that brain activity and eye activity are required during the viewing of the learning document by the user so that the required knowledge can be learned.
Specifically, the brain wave information and the eye movement information of the user may be acquired in many ways, and as one example, the brain wave information of the user is detected by a head-mounted brain wave detecting apparatus and the eye movement information of the user is detected by a camera. As another example, brain wave information and eye movement information of the user may be acquired by the relevant sensors.
And 102, integrating and analyzing the brain wave information and the eye activity information to determine the real-time learning state of the user.
And 103, marking the real-time learning state at the corresponding playing position in the learning file.
Specifically, after acquiring the brain wave information and the eye movement information of the user, it is necessary to perform integrated analysis on the information to determine the real-time learning state of the user. The brain wave information and the eye activity information can be integrated and analyzed in various ways to determine the real-time learning state of the user, and the selection and the setting can be carried out according to the actual application requirements.
In order to make it clear for those skilled in the art how to determine the real-time learning state of the user according to the integrated analysis of the brain wave information and the eye movement information, the following example is specifically described with reference to fig. 2 as follows:
FIG. 2 is a flow diagram of a method of determining a learning state of a user in real-time, according to one embodiment of the invention.
As shown in fig. 2, the method for determining the real-time learning state of the user includes:
And step 203, determining the real-time learning state of the user according to the real-time brain fatigue condition, sight line deviation condition or eye fatigue condition of the user.
Specifically, by analyzing the band index value in which the brain wave is located, one or more of a power value, a power percentage, a power ratio, a power sum ratio, and the like may be included, and simultaneously, a difference analysis (for example, ANOVA variability analysis) is performed on each index value in different fatigue states, and when the difference reaches an index of a preset fatigue state, the real-time brain fatigue condition of the user is determined.
The index of the preset fatigue state can be selected and set according to the actual application requirement, and is generally the index which is achieved by the difference of most people.
Specifically, by analyzing the real-time eye sight signals of the eye activity information, the real-time eye sight deviation condition (such as the time of eye sight deviation exceeding a certain time threshold) and the eye fatigue condition (such as the time of eye closure exceeding a certain time or the time of blinking exceeding a certain time) of the user can be determined.
Further, the real-time learning state of the user is determined according to the real-time brain fatigue condition, sight line deviation condition or eye fatigue condition of the user. Therefore, the real-time learning state of the user is determined to be marked at the corresponding playing position in the learning file, so that the learning state of the user can be monitored and remarked, the user can conveniently learn and review subsequently, and the learning quality of the user is improved.
It is to be understood that the learning state determined in real time by the user includes a normal learning state and may be an abnormal learning state. The embodiment of the invention mainly aims at marking the corresponding playing position in the learning file when the real-time learning state of the user is determined to be the abnormal learning state, so that the user can conveniently learn and review subsequently. It will be appreciated that the real-time learning status of the user may be determined to be an abnormal learning status in a number of ways, such as the following:
in a first example, if it is known from the analysis of the eye activity information that the eye closing time of the user is greater than a preset first time and less than a preset second time, and the number of times of the eye closing time is greater than a preset threshold, and it is known from the analysis of the electroencephalogram information of the corresponding time period that the user is drowsy, it is determined that the user is currently drowsy.
In a second example, if it is known that the eye-closing time of the user is greater than the preset second time according to the analysis of the eye activity information, and the user is in a drowsy state according to the analysis of the brain wave information in the corresponding time period, it is determined that the user is currently in a asleep state.
In a third example, if the eye movement information is analyzed and the eye signal of the user does not deviate from the learning file, whether the number of the blink signals of the user meets a preset threshold value is detected; when the number of the blink signals reaches a preset threshold value, calculating a variance value of the currently stored blink signal data, and comparing the variance value with a preset variance threshold value; and if the current blinking fatigue of the user is obtained according to the comparison result, and the drowsy state of the user is obtained according to the analysis of the brain wave information at the corresponding moment, determining that the user is in the drowsy state currently.
In a fourth example, if the fact that the sight line signal of the user deviates from the learning file is obtained through analysis according to the eye movement information, and the deviation time exceeds the preset time, it is determined that the user is in the sight line deviation state currently.
Specifically, whether the sight signal of the user does not deviate from the learning file or not is analyzed according to the eye movement information, namely whether the user is watching the learning file or not, and whether the number of blink signals of the user meets a preset threshold or not is detected when the sight signal of the user does not deviate from the learning file. The preset threshold value can be selected and set according to the actual application requirement.
As an example, a blink signal of a current user is obtained through a blink algorithm, the blink signal is stored in a data storage unit, a trigger signal is sent to a counter, when the number of the blink signals reaches a certain number, a variance value of the data of the blink signal stored currently can be calculated through a variance calculation formula in a calculation device, the variance value is compared with a preset variance threshold value in a threshold value storage unit, blink fatigue information of the user is obtained, primary judgment of the learning state of a learner is obtained, then the fact that the user is drowsy is obtained through brain wave information analysis, and the fact that the user is drowsy currently is determined.
It should be noted that, if it is known that the gaze signal of the user deviates from the learning file according to the eye movement information analysis, and the deviation time exceeds the preset time, it is determined that the user is currently in the gaze deviation state.
Specifically, the detection and judgment can be directly performed according to the sight signal, for example, if the detection result is that the deviation time of the sight signal exceeds the preset time, the user is directly judged to be in an abnormal learning state, the corresponding sight departure time and the playing position of the learning file when the sight signal departs are marked in the learning file marking, the corresponding abnormal learning state result is stored and marked, and if necessary, corresponding sound reminding or video flicker reminding is performed.
When the time for detecting that the eyes of the user are closed exceeds the first time and does not exceed the second time, and the frequency of exceeding the first time exceeds a certain threshold value, and the user is drowsy in the electroencephalogram detection state, the user is judged to be in a doze state, corresponding doze labels are carried out, the detection is continued, and the video progress is followed until the state is changed.
If the eye closing time is detected to exceed the second time and the brain wave detection result is drowsiness, the state is judged to be a sleep state, and corresponding marking and remarking are performed.
It should be noted that, when the real-time learning state of the user is determined to be the abnormal learning state, the user may be reminded to concentrate on learning through sound and/or light, so as to further improve the learning efficiency of the user.
It can be understood that, in the process of determining the states of the line-of-sight deviation, the drowsiness, the falling asleep, etc., a corresponding threshold needs to be set, for example, the line-of-sight signal deviation exceeds a preset time, so that the line-of-sight deviation in an abnormal state can be determined, and the misdetermination caused by the short-time line-of-sight deviation is avoided.
Therefore, the playing position of the user corresponding to the real-time learning state mark in the learning file can be determined, so that the learning state of the user can be monitored and remarked, the user can conveniently learn and review subsequently, and the learning quality of the user is improved.
Specifically, a corresponding playing position in the learning file is labeled according to a real-time learning state, and specific labeling is performed according to different learning states.
Note that, the label content may include a learning state, and more specifically, may label a learning state determination result of the user, which is obtained by detecting brain fatigue or a line of sight deviation of the user, and the learning state determination result may be normal, abnormal, and the like, and the abnormality may further include states of line of sight deviation, doze, falling asleep, and the like.
It should be noted that, the learning state can be marked regardless of the normal learning state or the abnormal state, and the corresponding learning file is stored regularly or regularly for later review, and targeted review and checking of the learning result are performed.
It should be noted that the user may be prompted to relearn the content of the file corresponding to the abnormal learning state marked in the learning file by voice, flashing lights or vibration, so as to avoid the problem that the learning quality is reduced due to the user missing the learning content.
It should be noted that, the prompt function can be selectively set to be turned on or off according to the actual application requirements.
In summary, according to the quality monitoring method based on electronic product learning in the embodiments of the present invention, the brain wave information and the eye activity information of the user are integrated and analyzed to determine the real-time learning state of the user to be marked at the corresponding playing position in the learning file, so that the learning state of the user can be monitored and remarked, the user can conveniently learn and review subsequently, and the learning quality of the user is improved.
Corresponding to the quality monitoring methods based on electronic product learning provided by the above embodiments, an embodiment of the present invention further provides a quality monitoring device based on electronic product learning, and since the quality monitoring device based on electronic product learning provided by the embodiment of the present invention corresponds to the quality monitoring methods based on electronic product learning provided by the above embodiments, the implementation of the quality monitoring method based on electronic product learning is also applicable to the quality monitoring device based on electronic product learning provided by the embodiment, and will not be described in detail in the embodiment.
Fig. 3 is a schematic structural diagram of a quality monitoring device based on electronic product learning according to an embodiment of the present invention.
As shown in fig. 3, the apparatus may include: an acquisition module 11, an analysis module 12 and a labeling module 13.
The obtaining module 11 is configured to obtain brain wave information and eye movement information of a user during a process that the user views a learning file through an electronic product.
The analysis module 12 is used for performing integrated analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user;
and the marking module 13 is configured to mark the real-time learning state at a corresponding playing position in the learning file.
In summary, the quality monitoring device based on electronic product learning according to the embodiment of the present invention determines the real-time learning state of the user to be marked at the corresponding playing position in the learning file by performing integrated analysis on the brain wave information and the eye activity information of the user, so that the learning state of the user can be monitored and remarked, which is convenient for the user to learn and review subsequently, and improves the learning quality of the user.
In order to realize the embodiment, the invention further provides a quality monitoring system based on the electronic product learning.
Fig. 4 is a schematic structural diagram of a quality monitoring system based on electronic product learning according to an embodiment of the present invention.
As shown in fig. 4, the system may include: the quality monitoring device 10 based on the electronic product learning, the brain wave detection device 20 and the eye movement detection device 30.
The quality monitoring device 10 based on electronic product learning is used for acquiring brain wave information and eye activity information of a user in the process that the user watches a learning file through an electronic product; integrating and analyzing the brain wave information and the eye activity information to determine the real-time learning state of the user; and marking the real-time learning state at the corresponding playing position in the learning file.
The brain wave detecting device 20 serves to detect brain wave information of the user.
The eye movement detection device 30 is used to detect the eye movement information of the user.
In order to make the brain wave detecting device 20 according to the embodiment of the present invention more clear to those skilled in the art, a specific example will be described below with reference to fig. 5.
Fig. 5 is a schematic structural view of a brain wave detecting apparatus according to an embodiment of the present invention. As shown in fig. 5, the brain wave detecting apparatus is wearable, and includes a sensing unit 1, an earphone body 2, and an information processing apparatus 3; as shown in fig. 6, the information processing apparatus 3 includes a brain wave processing unit, a filtering and noise reduction processing unit, and a data transmission unit.
In summary, the quality monitoring system based on electronic product learning according to the embodiment of the present invention determines the real-time learning state of the user to be marked at the corresponding playing position in the learning file by performing integrated analysis on the brain wave information and the eye activity information of the user, so that the learning state of the user can be monitored and remarked, which is convenient for the user to learn and review subsequently, and improves the learning quality of the user.
The invention provides a quality monitoring device based on electronic product learning, and fig. 7 is a schematic structural diagram of the quality monitoring device based on electronic product learning according to another embodiment of the invention. As shown in fig. 7, a memory 21, a processor 22, and a computer program stored on the memory 21 and executable on the processor 22.
The processor 22, when executing the program, implements the quality monitoring method based on electronic product learning provided in the above-described embodiments.
Further, the quality monitoring device based on electronic product learning further comprises:
a communication interface 23 for communication between the memory 21 and the processor 22.
A memory 21 for storing a computer program operable on the processor 22.
The memory 21 may comprise a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory.
And a processor 22, configured to implement the quality monitoring method based on electronic product learning according to the foregoing embodiment when executing the program.
If the memory 21, the processor 22 and the communication interface 23 are implemented independently, the communication interface 21, the memory 21 and the processor 22 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (enhanced Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 21, the processor 22 and the communication interface 23 are integrated on a chip, the memory 21, the processor 22 and the communication interface 23 may complete mutual communication through an internal interface.
The processor 22 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present invention.
In order to achieve the above object, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor on a server side, enable the server side to execute a quality monitoring method based on electronic product learning, the method including: acquiring brain wave information and eye activity information of a user in the process that the user watches a learning file through an electronic product; performing integration analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user; and marking the real-time learning state at a corresponding playing position in the learning file.
In order to achieve the above object, a fifth aspect of the present invention provides a computer program product, wherein when being executed by an instruction processor, the computer program product executes a quality monitoring method based on electronic product learning, and the method includes: acquiring brain wave information and eye activity information of a user in the process that the user watches a learning file through an electronic product; performing integration analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user; and marking the real-time learning state at a corresponding playing position in the learning file.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (9)
1. A quality monitoring method based on electronic product learning is characterized by comprising the following steps:
acquiring brain wave information and eye movement information of a user;
performing integration analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user;
marking the real-time learning state at a corresponding playing position in a learning file, and storing the corresponding learning file according to preset time; wherein, specific marking is carried out according to different learning states;
wherein, the integrating and analyzing the brain wave information and the eye movement information to determine the real-time learning state of the user includes:
analyzing the real-time waveband index values of the brain wave information, performing difference analysis on each index value, extracting indexes with differences reaching preset fatigue states, and determining the real-time brain fatigue condition of the user; analyzing the real-time sight signal of the eye activity information, and determining the real-time sight deviation condition or eye fatigue condition of the user; and determining the real-time learning state of the user according to the real-time brain fatigue condition, sight line deviation condition or eye fatigue condition of the user.
2. The method of claim 1, further comprising:
detecting brain wave information of the user by a head-mounted brain wave detecting device, an
And detecting the eye activity information of the user through a camera.
3. The method of claim 1, wherein the step of performing the integrated analysis of the brain wave information and the eye movement information to determine the real-time learning state of the user comprises:
and performing integrated analysis on the brain wave information and the eye activity information to determine that the real-time learning state of the user is an abnormal learning state.
4. The method as claimed in claim 3, wherein the step of performing the integrated analysis on the brain wave information and the eye activity information to determine that the real-time learning state of the user is an abnormal learning state comprises:
if the eye closing time of the user is larger than a preset first time and smaller than a preset second time according to the eye activity information analysis, the times of the eye closing time are larger than a preset threshold value, and the user is in a drowsy state according to the brain wave information analysis in the corresponding time period, determining that the user is in a doze state currently;
if the eye closing time of the user is larger than a preset second time according to the eye activity information analysis, and the user is in a drowsy state according to the brain wave information analysis of the corresponding time period, determining that the user is in a falling asleep state currently;
and if the fact that the sight signals of the user deviate from the learning file is analyzed and obtained according to the eye movement information, and the deviation time exceeds the preset time, determining that the user is in the sight deviation state at present.
5. The method as claimed in claim 3, wherein the step of performing the integrated analysis on the brain wave information and the eye activity information to determine that the real-time learning state of the user is an abnormal learning state comprises:
if the eye movement information is analyzed and obtained that the sight signals of the user do not deviate from the learning file, whether the number of the blink signals of the user meets a preset threshold value or not is detected;
when the number of the blink signals reaches a preset threshold value, calculating a variance value of the currently stored blink signal data, and comparing the variance value with a preset variance threshold value;
and acquiring the current blinking fatigue of the user according to the comparison result, analyzing and acquiring the drowsiness state of the user according to the brain wave information at the corresponding moment, and determining the drowsiness state of the user.
6. The method of claim 1, wherein the learning state comprises an abnormal learning state, the method further comprising:
and if the real-time learning state of the user is abnormal learning, reminding the user to concentrate on the mental learning through sound and/or light.
7. The method of claim 1, wherein the learning state comprises an abnormal learning state, the method further comprising:
and prompting the user to relearn the file content which is marked in the learning file and corresponds to the abnormal learning state.
8. A quality monitoring device based on electronic product learning, comprising:
the acquisition module is used for acquiring brain wave information and eye movement information of a user;
the analysis module is used for performing integrated analysis on the brain wave information and the eye activity information to determine the real-time learning state of the user;
the marking module is used for marking the real-time learning state at a corresponding playing position in a learning file and storing the corresponding learning file according to preset time; wherein, specific marking is carried out according to different learning states;
the analysis module is specifically configured to:
analyzing the real-time waveband index values of the brain wave information, performing difference analysis on each index value, extracting indexes with differences reaching preset fatigue states, and determining the real-time brain fatigue condition of the user; analyzing the real-time sight signal of the eye activity information, and determining the real-time sight deviation condition or eye fatigue condition of the user; and determining the real-time learning state of the user according to the real-time brain fatigue condition, sight line deviation condition or eye fatigue condition of the user.
9. A quality monitoring system based on electronic product learning, comprising:
a brain wave detecting device, an eye movement detecting device, and the electronic product learning-based quality monitoring device according to claim 8.
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| CN108310759B (en) * | 2018-02-11 | 2021-04-16 | Oppo广东移动通信有限公司 | Information processing method and related product |
| CN110658911A (en) * | 2018-06-29 | 2020-01-07 | 深圳市掌网科技股份有限公司 | Virtual reality course adjusting system and method, virtual reality helmet and processing device |
| CN109009173B (en) * | 2018-08-30 | 2022-02-01 | 北京机械设备研究所 | Fatigue detection and regulation method based on electroencephalogram-eye movement bimodal signals |
| CN109634422B (en) * | 2018-12-17 | 2022-03-01 | 广东小天才科技有限公司 | Recitation monitoring method and learning equipment based on eye movement recognition |
| CN109711550A (en) * | 2018-12-27 | 2019-05-03 | 秒针信息技术有限公司 | The labeling system of data, method and device, storage medium, electronic device |
| CN109871127A (en) * | 2019-02-15 | 2019-06-11 | 合肥京东方光电科技有限公司 | Display equipment and display information processing method based on human-computer interaction |
| CN110008874B (en) * | 2019-03-25 | 2021-05-18 | 联想(北京)有限公司 | Data processing method and device, computer system and readable medium |
| CN112307323B (en) * | 2020-03-02 | 2023-05-02 | 北京字节跳动网络技术有限公司 | Information pushing method and device |
| CN112150330B (en) * | 2020-09-24 | 2024-01-26 | 安徽智慧教育科技有限公司 | Internet-based supervised learning method and system |
| CN112306832A (en) * | 2020-10-27 | 2021-02-02 | 北京字节跳动网络技术有限公司 | User state response method and device, electronic equipment and storage medium |
| CN113509189A (en) * | 2021-07-07 | 2021-10-19 | 科大讯飞股份有限公司 | Learning state monitoring method and related equipment thereof |
| CN114241719B (en) * | 2021-12-03 | 2023-10-31 | 广州宏途数字科技有限公司 | Visual fatigue state monitoring method, device and storage medium in student learning |
| CN114668948B (en) * | 2022-03-24 | 2023-06-02 | 江苏省人民医院（南京医科大学第一附属医院） | Integrated vertical perception training instrument |
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| US20150286958A1 (en) * | 2015-06-23 | 2015-10-08 | Chien Cheng Liu | Interactive learning management method |
| CN104916176B (en) * | 2015-07-08 | 2019-01-01 | 广东小天才科技有限公司 | A kind of classroom sound pick-up outfit and the way of recording |
| CN105678959B (en) * | 2016-02-25 | 2018-06-15 | 重庆邮电大学 | A kind of fatigue driving monitoring method for early warning and system |
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