CN108937965B - Attention evaluation system and method based on sitting posture analysis - Google Patents
Attention evaluation system and method based on sitting posture analysis Download PDFInfo
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- CN108937965B CN108937965B CN201810413334.XA CN201810413334A CN108937965B CN 108937965 B CN108937965 B CN 108937965B CN 201810413334 A CN201810413334 A CN 201810413334A CN 108937965 B CN108937965 B CN 108937965B
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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
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1116—Determining posture transitions
Abstract
The invention discloses an attention assessment system and method based on sitting posture analysis, and relates to the technical field of educational psychometric measurement. The system mainly comprises a sitting posture analysis circuit module (1), an attention mapping module (2), a cloud storage module (3) and a data analysis and visualization module (4) which are connected in a data communication mode. The method for the system to evaluate the attention of the user comprises the following steps: acquiring user posture parameters; analyzing the current sitting posture of the user; evaluating the attention condition of the user according to the mapping relation between the sitting posture and the attention; attention data cloud storage; analyzing attention data; and visualizing the analysis result on the mobile phone App. The system realizes diversified functions of user attention monitoring, acquisition, storage, analysis and the like, provides a low-cost, high-efficiency and small-interference attention assessment mode for the user, and can be used for teaching big data collection. The problems of high cost, complex operation and the like caused by the fact that the existing attention assessment system mostly utilizes large monitoring tools such as an eye tracker and the like are effectively solved.
Description
Technical Field
The invention relates to the technical field of education psychometric measurement, in particular to a system and a method for attention assessment based on sitting posture analysis, which are suitable for education big data analysis.
Background
In the last 50 th century, the learner attention measurement mostly adopts a direct observation method or a parent and teacher interview method, but the method is excessively dependent on observation and personal level and experience of researchers, has greater subjectivity and has certain defects in the attention measurement process. Therefore, researchers more adopt attention measurement technologies with stronger objectivity and convenience in data acquisition and processing, and currently, common attention measurement and evaluation methods can be mainly classified as follows:
(1) and (4) measuring by using an instrument. Mainly based on the attention stability theory, the attention stability is measured and evaluated by detecting the overall physiological function condition of the nervous system or by combining computer hardware and software. The method mainly comprises electroencephalogram (EEG) detection, eye movement tracking test, continuous operation test (CPT), various newly developed software and hardware test methods and the like.
(2) And (5) a homework test method. The practice method usually guides the subject to perform a certain task by simulating the actual life creating situation, so that the subject actively expresses the individual psychological characteristics through the practice.
(3) Questionnaire scale method. The questionnaire scale method mainly takes paper pen test as a main part, carries out scale test in various modes such as self-evaluation, mutual evaluation and other evaluation, and has the problems of low operation cost and convenient test although the problems of over dependence on the subjective concept of a tested subject, high data processing complexity, instantaneity of data feedback and the like exist.
The analysis of the above various attention measurement methods can find that the general questionnaire scale and operation method are simple in operation and easy to implement, but have strong subjectivity, inconvenient data acquisition and processing and high time cost; although the instrumental measurement method can provide a more objective observation means and can avoid the influence of memory bias, halo effect or other confusion factors on a testee in the cognitive measurement process to a certain extent, most of the measurement methods have higher requirements on the test environment, and equipment such as an eye tracker, a brain wave imager and the like is high in price and complex in operation. All the data acquired by the measurement methods are 'one-time' attention data, and the requirement on data acquisition in the current big data environment is difficult to meet.
The most critical attention measurement methods ignore the characteristic of dynamic fluctuation of attention, and can only detect the attention condition of the testee in a specific environment, but cannot acquire the attention data of the testee in a common environment such as a real classroom scene in real time.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings in the prior art and provides an attention assessment system and method based on sitting posture analysis.
The basic idea of the invention is as follows:
based on the demonstration of the correlation between the sitting posture and the attention, the sitting posture is considered to reflect the attention of the learner to some extent. Meanwhile, the psychological research shows that the attention stability and the learning performance of the learner have obvious correlation among the four characteristics of the attention, and the quality of the attention stability plays an important role in the learning effect of the learner. Therefore, by analyzing the characteristic of the classroom attention of the learner, the performance characteristics of the dynamics and fluctuation of the attention are restored, a sitting posture-based learner attention mapping model is defined and constructed, a sitting posture-based learner attention evaluation tool is designed and developed, and the acquisition, storage and analysis of real-time classroom attention data are realized by combining with the application of modern network technologies such as a cloud platform technology and the like.
The invention relates to an attention evaluation system based on sitting posture analysis, which comprises a sitting posture analysis circuit module, an attention mapping module and a cloud storage module, wherein a data analysis and visualization module is connected in a data communication mode.
The sitting posture analysis circuit module is a chest card which collects real-time body posture data of a user and analyzes the current sitting posture of the user by utilizing the posture data;
the attention mapping module maps the sitting posture into attention data according to the corresponding relation between the sitting posture and the attention based on the user sitting posture;
the cloud storage module uses a cloud platform as a data storage cloud server and comprises data uploading, data storage and data issuing functions;
the data analysis and visualization module analyzes and processes the attention data of the user according to different dimensions and presents an analysis result in a chart form.
The chest card acquires user posture data, obtains a user sitting posture according to the posture data analysis, and then obtains user attention data according to the corresponding relation between the sitting posture and the attention;
the chest card is fixed at the chest position of the upper body of the user, and an angle sensor, a main processing chip and a wireless communication module are arranged in the chest card;
the angle sensor establishes an XYZ three-dimensional rectangular coordinate system according to a Roll-Pitch-Yaw model, and judges forward leaning and backward leaning, left leaning and right leaning, and left turning and right turning of the human body in sitting posture;
and receiving user posture data through digital communication by utilizing a main processing chip, and then judging the current user sitting posture through angle synthesis analysis.
After the attention data are collected and stored, the classroom attention condition of the learner is monitored in real time through mobile client software (APP), data are visually displayed from the angles of a sitting posture frequency statistical chart, an attention condition curve chart, a same sitting posture duration distribution chart (attention stability) and the like, an attention condition analysis suggestion is completed, and the data can provide big data analysis or later analysis in real time.
In conclusion, the attention assessment system and the attention assessment method based on sitting posture analysis are defined, constructed and developed based on a user attention mapping model of sitting posture and designed and developed based on the performance characteristics of the classroom attention of learners, and are combined with modern network technologies such as cloud platform technology and the like to realize the acquisition, storage and analysis of real-time classroom attention data and provide technical support for large data analysis or later analysis.
Drawings
FIG. 1 is a block diagram of an attention assessment system based on sitting posture analysis according to the present invention;
FIG. 2 is a block diagram of a module for mapping sitting posture data to attention data according to an embodiment of the present invention;
FIG. 3 is a diagram of an angle sensor model according to the present invention in a Roll-Pitch-Yaw model;
fig. 4 is a connection block diagram of a mobile APP function module according to an embodiment of the present invention.
Detailed Description
The invention is further described in the following with reference to the figures and examples
An attention assessment system based on sitting posture analysis (as shown in figure 1) comprises a sitting posture analysis circuit module 1, an attention mapping module 2, a cloud storage module 3 and a data analysis and visualization module 4 which are connected in a data communication mode.
The sitting posture analysis circuit module 1 collects real-time body posture data of a user through a chest card and analyzes the current sitting posture of the user by utilizing the posture data;
the attention mapping module 2 maps the sitting posture into attention data according to the corresponding relation between the sitting posture and the attention based on the user sitting posture;
the cloud storage module 3 uses a data cloud platform as a data storage cloud server and comprises data uploading, data storage and data issuing functions;
the data analysis and visualization module 4 analyzes and processes the attention data of the user according to different dimensions and presents the analysis result in a chart form.
The sitting posture data of the embodiment of the invention is mapped into an attention data module (as shown in figure 2). GY-25 is a low-cost inclination module based on an MPU-6050 space motion sensor chip. GY-25 is based on MPU-6050, and data of a gyroscope and an acceleration sensor are subjected to a data fusion algorithm to obtain final angle data, and the final angle data are directly output to a WeMOS-D1 single chip microcomputer through a serial port.
The sitting posture data sent by the GY-25 is processed by the single chip microcomputer according to a Roll-Pitch-Yaw model (shown in an attached figure 3), the Roll-Pitch-Yaw model is mainly used for flight control of an unmanned aerial vehicle or an aircraft, and the human body sitting posture condition is calculated by the aid of the model. When the model is applied to a human body sitting posture system, the rotation of the human body around the Z axis is identified by a course angle, namely the left turn and the right turn of the human body; the rotation of the human body around the Y axis is marked by a roll angle, namely, the left leaning and the right leaning; the rotation of the body about the X-axis, i.e. pitch and yaw, is identified by the pitch angle. Judging that the human body is marked to rotate around the Z axis by the course angle, and turning the human body left and right; marking the rotation of the human body around the Y axis by using the roll angle as a left inclination and a right inclination; the pitch angle marks the rotation of the human body around the X axis, namely forward leaning and backward leaning.
The single chip microcomputer is processed, and then the current user sitting posture is judged through angle synthesis analysis. And data are packaged according to an HTTP protocol by means of an ESP-8266 chip, and the data are connected with a wireless network, so that data transmission of the OneNet cloud server by hardware equipment is completed through the wireless network, and the data are stored on the cloud server. Next, data analysis and visual display can be performed by using a self-developed H5 App to acquire attention data and sitting posture data of relevant learners from the OneNet cloud server through an HTTP protocol, and relevant records can be viewed on the App.
Sitting posture attention data module 2 (shown in figure 1) is a set of data processing modules running in WeMOS-D1. Based on different sitting posture types and different attention corresponding to different sitting posture conversion time intervals, the main processing chip converts the user sitting posture obtained through analysis into corresponding attention scores according to the corresponding relation between the sitting posture and the attention, the process is executed once every 1 second, and the average number of the attention scores obtained 5 times is taken as the attention numerical value of the user in the period of time.
In the embodiment, when the current roll angle (namely the forward leaning and backward leaning angle of the human body) of the learner is measured to be less than-110 degrees (the roll angle is measured to be more than-110 and less than-90 degrees under the sitting range of the human body by experiments), the current sitting posture of the learner can be judged to be the backward leaning sitting posture; when the detected course angle (namely the angle when the human body rotates left and right) is greatly changed than the course angle at the previous moment, the current learner can be judged to be in a rotating state. After preliminary processing by WeMos-D1, the angle data obtained by GY-25 measurement can be converted into corresponding 9 sitting postures (sitting upright, leaning forward to the left, leaning forward to the right, leaning left, leaning right, leaning forward backward, leaning backward to the left, and leaning backward to the right) and 2 turning postures respectively.
After the sitting posture data is obtained, the WeMos-D1 converts the currently obtained sitting posture data into a corresponding attention score according to a defined sitting posture to attention mapping table. The specific algorithm is as follows: if the current one-second learner sitting posture is the same as the last one-second sitting posture, the current learner continues the attention condition of the last one-second, and the attention total score is directly added with the attention score corresponding to the sitting posture; if the learner's sitting posture changes from the last second, it means that the learner's attention is in a distracted state, and the total attention point cannot be added with the corresponding sitting posture point.
Finally, the attention level situation of the learner in the time is obtained. In the invention, the Wemos-D1 singlechip processes the GY-25 sensor data once every 1 second (namely, the current sitting posture condition of a learner is obtained once every second), continuously collects the sitting postures within five seconds, accumulates the attention scores corresponding to the five sitting postures, and uploads the average value of the attention scores as the current attention level score within 5 seconds to the cloud database for data storage.
The attention mapping module 2 mapping table resolution is as follows: and integrating the attention stability, the attention breadth, the attention concentration degree and the attention resource distribution condition indexes to obtain the current attention data of the user.
The sitting posture change means that the sitting posture of the previous second is different from the sitting posture of the current second;
the sitting posture types include nine types, specifically:
sitting right: the body facing direction is taken as the positive direction, the body of the user and the sitting surface form a sitting posture with a front-back difference not exceeding 20 degrees, and the sitting posture is close to 90 degrees;
forward inclining: taking the body facing direction as a positive direction, and enabling the included angle between the body of the user and the sitting surface to be less than 70 degrees and more than 50 degrees;
front bending: the body facing direction is taken as the positive direction, and the included angle between the body of the user and the sitting surface is less than 50 degrees;
left-leaning: taking the upward direction of the trunk of the body when the user sits positively as the positive direction, and deviating the position of the trunk to the left by more than 30 degrees when the user sits positively;
rightly inclining: taking the upward direction of the trunk of the body when the user sits positively as the positive direction, and rightwards deviating the position of the trunk by more than 30 degrees;
forward leaning and left leaning: left leaning in a forward leaning state;
forward leaning and right leaning: right leaning in a forward leaning state;
backward tilting: the body facing direction is taken as the positive direction, and the included angle between the body of the user and the sitting surface is more than 110 degrees;
turning: the body trunk is used as an axis, and the left-right rotation amplitude of the trunk exceeds 30 degrees.
The corresponding relationship between the sitting posture and the attention comprises the following steps:
when the sitting posture of the user is the positive sitting posture and the forward leaning posture, the attention is most focused;
when the user sits on the chair to lean forward left and right, the attention is more concentrated;
when the user sitting postures are left leaning and right leaning, the attention is less concentrated;
when the user sits forward, pronates and leans backward, the user is distracted;
when the user turns and changes his sitting posture, the user is most distracted.
The visualization platform of the data analysis and visualization module 4 is a mainstream Android platform or other platforms supporting HTML5 software.
The HTML5 plus technology relies on HBuilder to develop IDE, and utilizes mobile APP developed by an MUI front-end development framework to realize the following technical elements:
data downloading: when a user clicks or refreshes a page on a mobile App to generate a data request, the mobile App initiates a GET HTTP request to a data cloud platform by calling a jQuery bottom layer AJAX by using an Ajax () method so as to acquire data;
data analysis: after the mobile App obtains a JSON data packet issued by the data cloud platform, the mobile App analyzes the data packet and obtains the attention numerical value of each user time point in the analysis request time period;
data visualization: the mobile App utilizes a Baidu echarts data visualization chart interface to convert the attention data obtained by analysis into various charts.
The mobile APP function module (shown in FIG. 4) comprises an attention real-time monitoring module and an attention data analysis module;
wherein the content of the first and second substances,
attention real-time monitoring module: displaying the attention change condition and the real-time attention score of the user within five minutes before the moment in two data visualization modes of an attention curve graph and an attention instrument panel, and refreshing the interface once within 3 seconds;
attention data analysis module: the statistical data of the user sitting posture types are displayed mainly through a Nandingger rose diagram, the attention change condition of the user is displayed through an attention change curve diagram, and the attention stability of the user is displayed through an attention stability change curve diagram.
In summary, the attention assessment system and method based on sitting posture analysis, which are defined and constructed based on the sitting posture user attention mapping model and designed and developed, are connected in a data communication manner by the sitting posture analysis circuit module, the attention mapping module, the cloud storage module and the data analysis and visualization module. The method overcomes the defects that the prior various attention measurement methods ignore the characteristic of dynamic fluctuation of attention, can only detect the attention condition of a tested person in a specific environment, but cannot acquire the attention data of a real classroom scene of a user in a general environment in real time, and the like. The problems of high cost, complex operation and the like caused by the fact that the existing attention assessment system mostly utilizes large monitoring tools such as an eye tracker and the like are effectively solved. The method for the system to evaluate the attention of the user comprises the following steps: acquiring user posture parameters; analyzing the current sitting posture of the user; evaluating the attention condition of the user according to the mapping relation between the sitting posture and the attention; attention data cloud storage; analyzing attention data; and visualizing the analysis result on the mobile phone App. The system realizes diversified functions of monitoring, collecting, storing, analyzing and the like of the attention of the user, provides an attention assessment mode with low cost, high efficiency and small interference for the user, restores the dynamic fluctuation characteristic of the attention, and can be used for collecting big teaching data. The system realizes the real-time acquisition and storage of real-time data of attention in a classroom and provides technical support for analysis of big data or later analysis.
Claims (9)
1. The attention evaluation system based on sitting posture analysis is characterized by comprising a sitting posture analysis circuit module (1), an attention mapping module (2), a cloud storage module (3) and a data analysis and visualization module (4), wherein the data analysis and visualization module is connected in a data communication mode;
the sitting posture analysis circuit module (1) is a chest card which collects real-time body posture data of a user and analyzes the current sitting posture of the user by utilizing the posture data;
the attention mapping module (2) maps the sitting posture into attention data according to the corresponding relation between the sitting posture and the attention based on the user sitting posture; corresponding to different attentions based on different sitting posture types and sitting posture conversion time intervals, converting the user sitting posture obtained through analysis into a corresponding attention score according to the corresponding relation between the sitting posture and the attentions through a main processing chip;
the cloud storage module (3) uses a cloud platform as a data storage cloud server and comprises data uploading, data storage and data issuing functions;
the data analysis and visualization module (4) analyzes and processes the attention data of the user according to different dimensions and presents the analysis result in a chart form.
2. The attention assessment system based on sitting posture analysis as claimed in claim 1, wherein the chest card obtains user posture data, obtains user sitting posture according to the posture data analysis, and then obtains user attention data according to the corresponding relationship between the sitting posture and the attention;
the chest card is fixed at the chest position of the upper body trunk of the user or is placed on a hat or a helmet, and an angle sensor, a main processing chip and a wireless communication module are arranged in the chest card;
the angle sensor establishes an XYZ three-dimensional rectangular coordinate system according to a Roll-Pitch-Yaw model, and judges forward leaning and backward leaning, left leaning and right leaning, and left turning and right turning of the human body in sitting posture; and receiving user posture data through digital communication by utilizing a main processing chip, and then judging the current user sitting posture through angle synthesis analysis.
3. The attention assessment system based on sitting posture analysis as claimed in claim 1, wherein the data of the cloud storage module (3) is uploaded: the processed user attention data are packaged by the main processing chip and uploaded to the cloud platform through the wireless communication module; data storage: after receiving the uploaded attention data, the cloud platform analyzes the uploaded data, generates a user attention data point according to data uploading time, and records the user attention data uploading time and an attention score;
data issuing: and the cloud platform packages the data in the request analysis time period into a JSON data format according to a data analysis request initiated by a user and issues the data through an HTTP protocol.
4. The attention assessment system based on sitting posture analysis as claimed in claim 1, wherein the visualization platform of said data analysis and visualization module (4) is the mainstream Android platform or other HTML5 supporting software platform.
5. The attention assessment system based on sitting posture analysis as claimed in claim 4, wherein the HTML5 software, relying on HBuilder to develop IDE, utilizes MUI front end development framework to develop mobile APP, realizes the following technical elements:
data downloading: when a user clicks or refreshes a page on a mobile App to generate a data request, the mobile App initiates a GET HTTP request to a cloud platform by calling a jQuery bottom layer AJAX by using an Ajax () method so as to acquire data;
data analysis: after the mobile App obtains a JSON data packet issued by the cloud platform, the mobile App analyzes the data packet to obtain the attention numerical value of each time point of the user in the analysis request time period;
data visualization: the mobile App utilizes a Baidu echarts data visualization chart interface to convert the attention data obtained by analysis into various charts.
6. The attention assessment system based on sitting posture analysis as claimed in claim 5, wherein said mobile APP comprises an attention real-time monitoring module and an attention data analysis module;
wherein, attention real-time monitoring module: displaying the attention change condition and the real-time attention score of the user within five minutes before the current moment in two data visualization modes of a curve chart and a dashboard, and refreshing the interface once within 3 seconds;
attention data analysis module: the statistical data of the user sitting posture types are displayed mainly through a Nandingger rose diagram, the attention change condition of the user is displayed through a curve diagram, and the attention stability of the user is displayed through a difference curve diagram.
7. The attention assessment system based on sitting posture analysis as claimed in claim 1, wherein the sitting posture is used as an external observation variable of the attention condition, the attention condition of the user is grasped according to the corresponding relationship between the sitting posture and the attention, and the specific assessment method comprises: calibrating the current attention stability of the user according to the sitting posture change times of the user;
calibrating the current attention breadth, attention concentration degree and attention resource allocation condition of the user according to the sitting posture type of the user; the current user current attention stability, attention breadth, attention concentration degree and attention resource distribution condition indexes are integrated to obtain
Attention data.
8. The attention assessment system based on sitting posture analysis as claimed in claim 7, wherein said sitting posture change means that the sitting posture at the previous moment is different from the sitting posture at the current moment; the time interval can be adjusted as required;
the sitting posture types include nine types, specifically:
sitting right: the body facing direction is taken as the positive direction, the body of the user and the sitting surface form a sitting posture with a front-back difference not exceeding 20 degrees, and the sitting posture is close to 90 degrees;
forward inclining: taking the body facing direction as a positive direction, and enabling the included angle between the body of the user and the sitting surface to be less than 70 degrees and more than 50 degrees;
front bending: the body facing direction is taken as the positive direction, and the included angle between the body of the user and the sitting surface is less than 50 degrees;
left-leaning: taking the upward direction of the trunk of the body when the user sits positively as the positive direction, and deviating the position of the trunk to the left by more than 30 degrees when the user sits positively;
rightly inclining: taking the upward direction of the trunk of the body when the user sits positively as the positive direction, and rightwards deviating the position of the trunk by more than 30 degrees;
forward leaning and left leaning: left leaning in a forward leaning state;
forward leaning and right leaning: right leaning in a forward leaning state;
backward tilting: the body facing direction is taken as the positive direction, and the included angle between the body of the user and the sitting surface is more than 110 degrees;
turning: the body trunk is used as an axis, and the left-right rotation amplitude of the trunk exceeds 30 degrees.
9. The attention assessment system based on sitting posture analysis as claimed in claim 8, wherein said sitting posture and attention corresponding relationship comprises:
when the sitting posture of the user is the positive sitting posture and the forward leaning posture, the attention is most focused;
when the user sits on the chair to lean forward left and right, the attention is more concentrated;
when the user sitting postures are left leaning and right leaning, the attention is less concentrated;
when the user sits forward, pronates and leans backward, the user is distracted;
when the user turns and changes his sitting posture, the user is most distracted.
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