CN112754437A - Concentration degree evaluation method and device, terminal equipment and readable storage medium - Google Patents
Concentration degree evaluation method and device, terminal equipment and readable storage medium Download PDFInfo
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Abstract
The invention discloses a concentration degree evaluation method, which comprises the following steps: acquiring at least one pulse data of a person to be detected within a preset time range, and selecting target pulse data according to the at least one pulse data; obtaining a plurality of standard pulse data under different concentration levels, and combining the plurality of standard pulse data to obtain a standard pulse data set; comparing the target pulse data with the standard pulse data in the standard pulse data set one by one to obtain a plurality of pulse difference values; and respectively inputting the pulse difference values into a clustering model to obtain a clustering result, and obtaining a concentration degree grade classification evaluation result according to a machine learning model and the clustering result. The concentration degree evaluation method provided by the invention combines the clustering model and the machine learning model, and judges the concentration degree based on the pulse data, so that the cost of the judgment method is reduced, and the precision of the judgment result is improved.
Description
Technical Field
The invention relates to the technical field of software detection, in particular to a concentration degree evaluation method, a concentration degree evaluation device, terminal equipment and a readable storage medium.
Background
Concentration is an embodiment of human intelligent behavior, and is a psychological state when a person concentrates on a certain thing or activity. Through the monitoring to concentration degree, computational efficiency that can be scientific, and then the time of predicting the completion task has the significance to the supplementary plan of making.
Currently, methods for evaluating human concentration include questionnaire survey, observation, computer vision, and the like. The questionnaire survey method mainly depends on subjective judgment, lacks scientific basis, and has low efficiency and low accuracy. The observation method generally installs the camera in the front of the monitor, monitors the monitor's state through the camera to whether the monitor has abnormal behavior, closes the eye behavior and judges the attentiveness of monitor according to, but because the monitor is constantly adjusting the state, the accuracy of judgement result also is difficult to guarantee. The computer vision method is developed based on artificial intelligence, is immature at present, not only has high development cost, but also is difficult to ensure the accuracy of a judgment result. Therefore, how to provide a method with scientific basis and capable of accurately judging the concentration degree is a problem to be solved urgently at present.
Disclosure of Invention
The invention aims to provide a concentration degree evaluation method, a concentration degree evaluation device, terminal equipment and a readable storage medium. According to the method, the pulse data of the monitored person is acquired through the pulse sensor, and the concentration degree of the pulse data is judged through the clustering model and the machine learning model, so that the cost of the judging method is reduced, and the precision of the judging result is improved.
In order to overcome the defects in the prior art, an embodiment of the present invention provides a concentration degree evaluation method, including:
acquiring at least one pulse data of a person to be detected within a preset time range, and selecting target pulse data according to the at least one pulse data;
obtaining a plurality of standard pulse data under different concentration levels, and combining the plurality of standard pulse data to obtain a standard pulse data set;
comparing the target pulse data with the standard pulse data in the standard pulse data set one by one to obtain a plurality of pulse difference values;
and respectively inputting the pulse difference values into a clustering model to obtain a clustering result, and obtaining a concentration degree grade classification evaluation result according to a machine learning model and the clustering result.
Preferably, the selecting the target pulse data according to the at least one pulse data includes:
when the number of the pulse data is one, selecting the current pulse data as target pulse data;
and when the number of the pulse data exceeds one, taking the average value of the pulse data as target pulse data.
Preferably, in the concentration evaluation method, the clustering model is constructed by a historical standard pulse data set and a preset concentration label.
Preferably, in the concentration degree evaluation method, the pulse data is acquired by a pulse sensor, and the pulse data includes a pulse frequency, a pulse signal maximum value and a pulse signal minimum value.
An embodiment of the present invention further provides a concentration degree evaluation apparatus, including:
the target pulse data acquisition unit is used for acquiring at least one pulse data of a person to be detected within a preset time range and selecting the target pulse data according to the at least one pulse data;
the standard pulse data set acquisition unit is used for acquiring a plurality of standard pulse data under different concentration levels, and combining the plurality of standard pulse data to obtain a standard pulse data set;
the pulse difference value calculating unit is used for comparing the target pulse data with the standard pulse data in the standard pulse data set one by one to obtain a plurality of pulse difference values;
and the concentration degree evaluation result acquisition unit is used for respectively inputting the pulse difference values into a clustering model to obtain a clustering result, and obtaining a concentration degree grade classification evaluation result according to the machine learning model and the clustering result.
Preferably, in the concentration degree evaluation apparatus, the target pulse data acquisition unit is further configured to:
when the number of the pulse data is one, selecting the current pulse data as target pulse data;
and when the number of the pulse data exceeds one, taking the average value of the pulse data as target pulse data.
Preferably, in the concentration evaluation device, the clustering model is constructed by a historical standard pulse data set and a preset concentration label.
Preferably, in the concentration degree evaluation device, the pulse data is acquired by a pulse sensor, and the pulse data includes a pulse frequency, a pulse signal maximum value, and a pulse signal minimum value.
An embodiment of the present invention further provides a computer terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the concentration assessment method as described in any one of the above.
An embodiment of the invention also provides a computer-readable storage medium having stored thereon a computer program for execution by a processor to implement the concentration assessment method as described in any of the above.
Compared with the prior art, the embodiment of the invention has the beneficial effects that: according to the method, the pulse data of the monitored person is acquired through the pulse sensor, and the concentration degree of the pulse data is judged through the clustering model and the machine learning model, so that the cost of the judging method is reduced, and the precision of the judging result is improved.
Drawings
FIG. 1 is a schematic flow chart of a method for concentration assessment according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a procedure for obtaining a standard pulse data set according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a process for collecting pulse data according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a concentration degree evaluation apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments 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 of the 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, an embodiment of the invention provides a method for concentration assessment, including:
s10, acquiring at least one pulse data of the person to be detected within a preset time range, and selecting target pulse data according to the at least one pulse data;
first, the pulse refers to the pulse of the artery, and the pulse data is change data for recording the pulse when the artery is pulsed. The pulse data is mainly obtained through a pulse sensor, and the pulse sensor is a sensor for detecting pulse related signals and is used for detecting pressure change generated during the pulse of an artery and converting the pressure change into an electric signal which can be observed and detected more intuitively.
Specifically, the pulse sensor works by measuring the fine vibration displacement and frequency generated when the fluid pressure is applied, acquiring a wireless signal reflecting the blood vessel micro vibration of a measuring point, restoring the wireless signal recording the blood vessel micro vibration into electric signal data, sequentially performing high-pass filtering, signal amplification, low-pass filtering and digital-to-analog conversion, and determining the blood vessel micro vibration data as pulse data. The high-pass filtering, the signal amplification, the low-pass filtering and the digital-to-analog conversion can be realized by a signal conditioning circuit, and the aim is to reduce signal interference and obtain a recognizable stable signal. The pulse sensor can be generally worn on the wrist, the neck, the ankle and the like of a human body, and can be worn at various positions as required for calculating the accuracy of the pulse data, and the position is not limited at all.
Furthermore, the type of the pulse sensor comprises an analog output mode and a digital output mode according to the output mode; the method can be mainly divided into a piezoelectric type, a piezoresistive type and a photoelectric type according to a signal acquisition mode, wherein the photoelectric type is mostly infrared light; in practical applications, the selection can be made according to specific requirements, and is not limited herein.
In addition, can set up a plurality of pulse sensors simultaneously and gather a plurality of pulse data in this step, but the result of gathering data usually can appear different situation, consequently can adopt different methods to select target pulse data under different situations, specifically be:
when only one pulse data is acquired, directly taking the current pulse data as the target pulse data for the subsequent steps; when more than one pulse data is acquired, the average value of these data is taken as the target pulse data. For example, when the pulse data is the pulse frequency, the number of the collected pulse data is 3, the frequencies of the 3 pulse data with the frequency are respectively 70 times/minute, 60 times/minute and 80 times/minute, and the average value of the three numbers is 70 times/minute at this time, that is, the frequency of the target pulse data is 70 times/minute at this time.
S20, obtaining a plurality of standard pulse data under different concentration levels, and combining the standard pulse data to obtain a standard pulse data set;
it should be noted that concentration is an embodiment of human intelligence, and is a psychological state when a person concentrates on a certain thing or activity. In the process of concentration evaluation, there is usually a scientific grade division for the concentration to assist the concentration evaluation process. In the step, a plurality of standard pulse data under different concentration levels are obtained and then combined to obtain a standard pulse data set. Wherein the standard pulse data set is a data collection that may have multiple sets of standard pulse data.
S30, comparing the target pulse data with the standard pulse data in the standard pulse data set one by one to obtain a plurality of pulse difference values;
it can be understood that the target pulse data is compared with the standard pulse data set to obtain the pulse data difference, wherein the more the number of the pulse data included in the standard pulse data set is, the more the attention degrees of different degrees are corresponded, which is beneficial to the classification evaluation of the judgment attention degree category corresponding to the target pulse data, and the accuracy of the judgment attention degree category is ensured. Therefore, different comparison results can be obtained for different data situations, including:
when the number of the pulse data sets in the standard pulse data set is sufficient, the target pulse data can be matched with one standard pulse data in the standard pulse data set, and the pulse data difference is 0;
when the quantity of the pulse data in the standard pulse data set is not enough, determining the pulse data difference between the target pulse data and the standard pulse data set, namely the difference value between the target pulse data and the standard pulse data set;
and S40, respectively inputting the pulse difference values into a clustering model to obtain clustering results, and obtaining concentration degree grade classification evaluation results according to a machine learning model and the clustering results.
It should be noted that the clustering model can obtain new knowledge or skills by studying how a computer simulates or realizes human learning behaviors, and reorganize the existing knowledge structure to continuously improve the performance of the clustering model, so that the clustering model has a certain capability of automatically judging certain articles. The clustering model in the embodiment of the invention is obtained based on the previous concentration pulse data set and the pre-allocated concentration label, and is used for realizing a model for judging concentration category classification, and further predicting the concentration classification evaluation according to the pulse data difference through the machine learning model. The clustering algorithm commonly comprises a K-means clustering algorithm, a system clustering algorithm and a DBSCAN algorithm, and the machine learning model mainly comprises a supervised learning model and an unsupervised learning model, wherein the supervised learning mainly comprises models for classification and regression: the classification models comprise linear classifiers (such as LR), Support Vector Machines (SVM), Naive Bayes (NB), K Nearest Neighbors (KNN), Decision Trees (DT), integrated models (RF/GDBT, etc.); regression models include linear regression, Support Vector Machine (SVM), K-nearest neighbor (KNN), regression tree (DT), and integrated model (ExtraTrees/RF/GDBT); the unsupervised learning model mainly comprises: data clustering (K-means)/data dimensionality reduction (PCA), and the like. The clustering model and the machine learning model may be selected according to practical applications, and are not limited herein.
According to the embodiment of the invention, the pulse data of the monitored person is acquired through the pulse sensor, and the concentration degree of the pulse data is judged through the clustering model and the machine learning model, so that the cost of the judging method is reduced, and the precision of the judging result is improved.
Referring to fig. 2, in an exemplary embodiment, the step of obtaining the normative pulse data set includes:
s201, determining pulse data of a plurality of concentration degrees;
s202, aggregating the pulse data with the plurality of concentration degrees as the standard pulse data set.
It is understood that the pulse data is monitored in a state where the concentration of the monitor is different, and the pulse data corresponding to the concentration is determined based on the concentration. In particular, determining pulse data of at least one concentration degree may comprise: performing a plurality of tests of concentration degree, wherein one test obtains pulse data of one corresponding concentration degree; pulse data for a plurality of attentions is determined.
Referring to fig. 3, in an exemplary embodiment, the pulse data includes a pulse frequency 21, a maximum pulse signal value 22, and a minimum pulse signal value 23.
It should be noted that the pulse frequency 21 is used to indicate the current state of the monitor, and since the pulse of a normal person is generally consistent with the heart rate, the change of the pulse frequency 21 can also be used to monitor the psychological state of the monitor, and by judging the psychological state (concentration) of the monitor, the work efficiency or learning efficiency can be scientifically calculated, and the completion time can be scientifically predicted.
Specifically, the pulse data may further include a pulse signal maximum value 22 and a pulse signal minimum value 23, the shape of the pulse data is similar to a wave shape, and the pulse signal maximum value 22 is used for indicating the highest signal value of the pulse data, namely the waveform peak value; the pulse signal minimum 23 is used to indicate the lowest signal value of the pulse data, i.e. the waveform trough.
It is understood that the maximum value 22 and the minimum value 23 of the pulse signal may represent the condition of the pulse data under certain conditions, and since the pulse of a normal person generally coincides with the heart rate, the health condition of the monitored person may also be determined by monitoring the change condition of the pulse data.
In a second aspect:
referring to fig. 4, in an exemplary embodiment, there is also provided a concentration evaluation apparatus 100, including:
the target pulse data acquisition unit 01 is used for acquiring at least one pulse data of a person to be detected within a preset time range and selecting the target pulse data according to the at least one pulse data;
a standard pulse data set obtaining unit 02, configured to obtain multiple standard pulse data at different concentration levels, and combine the multiple standard pulse data to obtain a standard pulse data set;
the pulse difference value calculating unit 03 is configured to compare the target pulse data with the standard pulse data in the standard pulse data set one by one to obtain a plurality of pulse difference values;
the concentration degree evaluation result obtaining unit 04 is configured to input the plurality of pulse difference values to a clustering model respectively to obtain a clustering result, and obtain a concentration degree grade classification evaluation result according to a machine learning model and the clustering result.
In this embodiment, it should be noted that the units 01 to 04 of the evaluation device are respectively used to execute the steps S10 to S40, specifically:
the target pulse data obtaining unit 01 performs step S10, obtains at least one pulse data of the person under test within a preset time range, and selects the target pulse data according to the at least one pulse data;
the pulse refers to an arterial pulse, and the pulse data is change data for recording a pulse when the arterial pulse is detected. The pulse data is mainly obtained through a pulse sensor, and the pulse sensor is a sensor for detecting pulse related signals and is used for detecting pressure change generated during the pulse of an artery and converting the pressure change into an electric signal which can be observed and detected more intuitively.
Specifically, the pulse sensor works by measuring the fine vibration displacement and frequency generated when the fluid pressure is applied, acquiring a wireless signal reflecting the blood vessel micro vibration of a measuring point, restoring the wireless signal recording the blood vessel micro vibration into electric signal data, sequentially performing high-pass filtering, signal amplification, low-pass filtering and digital-to-analog conversion, and determining the blood vessel micro vibration data as pulse data. The high-pass filtering, the signal amplification, the low-pass filtering and the digital-to-analog conversion can be realized by a signal conditioning circuit, and the aim is to reduce signal interference and obtain a recognizable stable signal. The pulse sensor can be generally worn on the wrist, the neck, the ankle and the like of a human body, and can be worn at various positions as required for calculating the accuracy of the pulse data, and the position is not limited at all.
Furthermore, the type of the pulse sensor comprises an analog output mode and a digital output mode according to the output mode; the method can be mainly divided into a piezoelectric type, a piezoresistive type and a photoelectric type according to a signal acquisition mode, wherein the photoelectric type is mostly infrared light; in practical applications, the selection can be made according to specific requirements, and is not limited herein.
In addition, can set up a plurality of pulse sensors simultaneously and gather a plurality of pulse data in this step, but the result of gathering data usually can appear different situation, consequently can adopt different methods to select target pulse data under different situations, specifically be:
when only one pulse data is acquired, directly taking the current pulse data as the target pulse data for the subsequent steps; when more than one pulse data is acquired, the average value of these data is taken as the target pulse data. For example, when the pulse data is the pulse frequency, the number of the collected pulse data is 3, the frequencies of the 3 pulse data with the frequency are respectively 70 times/minute, 60 times/minute and 80 times/minute, and the average value of the three numbers is 70 times/minute at this time, that is, the frequency of the target pulse data is 70 times/minute at this time.
The standard pulse data set obtaining unit 02 is configured to perform step S20, obtain a plurality of standard pulse data at different concentration levels, and combine the plurality of standard pulse data to obtain a standard pulse data set;
it should be noted that concentration is an embodiment of human intelligence, and is a psychological state when a person concentrates on a certain thing or activity. In the process of concentration evaluation, there is usually a scientific grade division for the concentration to assist the concentration evaluation process. In the step, a plurality of standard pulse data under different concentration levels are obtained and then combined to obtain a standard pulse data set. Wherein the standard pulse data set is a data collection that may have multiple sets of standard pulse data.
A pulse difference value calculating unit 03, configured to execute step S30, compare the target pulse data with the standard pulse data in the standard pulse data set one by one, to obtain a plurality of pulse difference values;
it can be understood that the target pulse data is compared with the standard pulse data set to obtain the pulse data difference, wherein the more the number of the pulse data included in the standard pulse data set is, the more the attention degrees of different degrees are corresponded, which is beneficial to the classification evaluation of the judgment attention degree category corresponding to the target pulse data, and the accuracy of the judgment attention degree category is ensured. Therefore, different comparison results can be obtained for different data situations, including:
when the number of the pulse data sets in the standard pulse data set is sufficient, the target pulse data can be matched with one standard pulse data in the standard pulse data set, and the pulse data difference is 0;
when the quantity of the pulse data in the standard pulse data set is not enough, determining the pulse data difference between the target pulse data and the standard pulse data set, namely the difference value between the target pulse data and the standard pulse data set;
a concentration evaluation result obtaining unit 04, configured to perform step S40, and input the pulse differences into a clustering model respectively to obtain clustering results, and obtain concentration level classification evaluation results according to a machine learning model and the clustering results.
It should be noted that the clustering model can obtain new knowledge or skills by studying how a computer simulates or realizes human learning behaviors, and reorganize the existing knowledge structure to continuously improve the performance of the clustering model, so that the clustering model has a certain capability of automatically judging certain articles. The clustering model in the embodiment of the invention is obtained based on the previous concentration pulse data set and the pre-allocated concentration label, and is used for realizing a model for judging concentration category classification, and further predicting the concentration classification evaluation according to the pulse data difference through the machine learning model. The clustering algorithm commonly comprises a K-means clustering algorithm, a system clustering algorithm and a DBSCAN algorithm, and the machine learning model mainly comprises a supervised learning model and an unsupervised learning model, wherein the supervised learning mainly comprises models for classification and regression: the classification models comprise linear classifiers (such as LR), Support Vector Machines (SVM), Naive Bayes (NB), K Nearest Neighbors (KNN), Decision Trees (DT), integrated models (RF/GDBT, etc.); regression models include linear regression, Support Vector Machine (SVM), K-nearest neighbor (KNN), regression tree (DT), and integrated model (ExtraTrees/RF/GDBT); the unsupervised learning model mainly comprises: data clustering (K-means)/data dimensionality reduction (PCA), and the like. The clustering model and the machine learning model may be selected according to practical applications, and are not limited herein.
In one exemplary embodiment, the standard pulse data set obtaining unit 02 is further configured to perform the following steps:
s201, determining pulse data of a plurality of concentration degrees;
s202, aggregating the pulse data with the plurality of concentration degrees as the standard pulse data set.
It is understood that the pulse data is monitored in a state where the concentration of the monitor is different, and the pulse data corresponding to the concentration is determined based on the concentration. In particular, determining pulse data of at least one concentration degree may comprise: performing a plurality of tests of concentration degree, wherein one test obtains pulse data of one corresponding concentration degree; pulse data for a plurality of attentions is determined.
In one exemplary embodiment, the pulse data in the concentration assessment device includes a pulse frequency 21, a pulse signal maximum 22, and a pulse signal minimum 23.
It should be noted that the pulse frequency 21 is used to indicate the current state of the monitor, and since the pulse of a normal person is generally consistent with the heart rate, the change of the pulse frequency 21 can also be used to monitor the psychological state of the monitor, and by judging the psychological state (concentration) of the monitor, the work efficiency or learning efficiency can be scientifically calculated, and the completion time can be scientifically predicted.
Specifically, the pulse data may further include a pulse signal maximum value 22 and a pulse signal minimum value 23, the shape of the pulse data is similar to a wave shape, and the pulse signal maximum value 22 is used for indicating the highest signal value of the pulse data, namely the waveform peak value; the pulse signal minimum 23 is used to indicate the lowest signal value of the pulse data, i.e. the waveform trough.
It is understood that the maximum value 22 and the minimum value 23 of the pulse signal may represent the condition of the pulse data under certain conditions, and since the pulse of a normal person generally coincides with the heart rate, the health condition of the monitored person may also be determined by monitoring the change condition of the pulse data.
In a third aspect:
in one exemplary embodiment, there is also provided a computer terminal device including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the concentration assessment method as described above.
The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the concentration degree evaluation method. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the concentration evaluation method according to any of the embodiments described above, and achieve technical effects consistent with the methods described above.
In certain exemplary embodiments, there is also provided a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the concentration assessment method according to any of the above embodiments. For example, the computer-readable storage medium may be the above-mentioned memory including program instructions executable by the processor of the computer terminal device to perform the concentration assessment method according to any of the above-mentioned embodiments, and to achieve technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for concentration assessment, comprising:
acquiring at least one pulse data of a person to be detected within a preset time range, and selecting target pulse data according to the at least one pulse data;
obtaining a plurality of standard pulse data under different concentration levels, and combining the plurality of standard pulse data to obtain a standard pulse data set;
comparing the target pulse data with the standard pulse data in the standard pulse data set one by one to obtain a plurality of pulse difference values;
and respectively inputting the pulse difference values into a clustering model to obtain a clustering result, and obtaining a concentration degree grade classification evaluation result according to a machine learning model and the clustering result.
2. The concentration assessment method according to claim 1, wherein the selecting target pulse data from the at least one pulse data comprises:
when the number of the pulse data is one, selecting the current pulse data as target pulse data;
and when the number of the pulse data exceeds one, taking the average value of the pulse data as target pulse data.
3. The concentration assessment method of claim 1, wherein the clustering model is constructed from historical standard pulse data sets and preset concentration labels.
4. The concentration assessment method of claim 1, wherein the pulse data is obtained by a pulse sensor, the pulse data comprising a pulse frequency, a pulse signal maximum and a pulse signal minimum.
5. A concentration assessment apparatus, comprising:
the target pulse data acquisition unit is used for acquiring at least one pulse data of a person to be detected within a preset time range and selecting the target pulse data according to the at least one pulse data;
the standard pulse data set acquisition unit is used for acquiring a plurality of standard pulse data under different concentration levels, and combining the plurality of standard pulse data to obtain a standard pulse data set;
the pulse difference value calculating unit is used for comparing the target pulse data with the standard pulse data in the standard pulse data set one by one to obtain a plurality of pulse difference values;
and the concentration degree evaluation result acquisition unit is used for respectively inputting the pulse difference values into a clustering model to obtain a clustering result, and obtaining a concentration degree grade classification evaluation result according to the machine learning model and the clustering result.
6. The concentration assessment apparatus according to claim 5, wherein the target pulse data acquisition unit is further configured to:
when the number of the pulse data is one, selecting the current pulse data as target pulse data;
and when the number of the pulse data exceeds one, taking the average value of the pulse data as target pulse data.
7. The concentration assessment apparatus of claim 5, wherein the clustering model is constructed from a historical standard pulse data set and a preset concentration label.
8. The concentration assessment device of claim 5, wherein the pulse data is obtained by a pulse sensor, the pulse data comprising a pulse frequency, a pulse signal maximum and a pulse signal minimum.
9. A computer terminal device, comprising: one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of attentiveness assessment of any of claims 1-4.
10. A computer-readable storage medium, on which a computer program is stored, which computer program is executed by a processor for implementing the concentration assessment method according to any one of claims 1-4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113827243A (en) * | 2021-11-29 | 2021-12-24 | 江苏瑞脑启智医疗科技有限公司 | Attention assessment method and system |
| CN117113241A (en) * | 2023-05-12 | 2023-11-24 | 中南大学 | Intelligent leakage monitoring method based on edge learning |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113827243A (en) * | 2021-11-29 | 2021-12-24 | 江苏瑞脑启智医疗科技有限公司 | Attention assessment method and system |
| CN113827243B (en) * | 2021-11-29 | 2022-04-01 | 江苏瑞脑启智医疗科技有限公司 | Attention assessment method and system |
| CN117113241A (en) * | 2023-05-12 | 2023-11-24 | 中南大学 | Intelligent leakage monitoring method based on edge learning |
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