CN108937965A - A kind of attention evaluation system and method based on sitting posture analysis - Google Patents
A kind of attention evaluation system and method based on sitting posture analysis Download PDFInfo
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Abstract
A kind of attention evaluation system and method based on sitting posture analysis of the present invention, is related to Educational Psychology field of measuring technique.System of the present invention mainly includes sitting posture analysis circuit module (1), and attention mapping block (2), cloud storage module (3), data analysis and visualization model (4) are connected with data communication method.The method that system carries out attention assessment to user includes: the acquisition of user's attitude parameter；Analyze the current sitting posture of user；User's attention situation is assessed with attention mapping relations according to sitting posture；Attention data cloud storage；Notice that force data is analyzed；The visual analyzing result on mobile phone A pp.Realize the diversified functions such as the monitoring of user's attention, acquisition, storage, analysis, for user provide low cost, high efficiency, small interference attention assessment mode, the big data that can be used for imparting knowledge to students collection.It effectively makes up existing attention evaluation system and utilizes the problems such as at high cost, complicated for operation caused by the large-scale monitoring instrument such as eye tracker more.
Description
Technical field
The present invention relates to Educational Psychology field of measuring technique, refer specifically to a kind of attention evaluation system based on sitting posture analysis
And method, it is suitable for education big data analysis.
Background technique
The fifties in last century, the measurement of learner's attention mostly use greatly direct observational method or parent, teacher's interview
Method, but such methods excessively rely on observation and the individual's level and experience of researcher, and there are larger subjectivities, measure in attention
There is certain defect in the process.Therefore researcher more use objectivity it is stronger, convenient for data acquisition and processing (DAP) attention survey
Amount technology, attention measurement common at present and evaluation method can be mainly classified as:
(1) instrument measuring method.The main Theory of Stability according to attention, passes through the whole physiological function for detecting nervous system
Situation or by computer hardware, software combine in the way of, measure and evaluate for pays attention stably.Mainly there is electroencephalogram
(EEG) detection, eye movement trace test, continuous performance task (CPT) and all kinds of newly developed software and hardware methods of testing etc..
(2) performance test method.Working system usually passes through simulation actual life set up situation, and guidance subject is engaged in a certain work
Industry, so that subject be made actively to show individual psychology feature by operation.
(3) questionnaire scaling method.Questionnaire scaling method mainly based on paper pencil test, by self-appraisal, mutually comment, a variety of sides such as he comments
Formula carries out scale test, although being tested subjective intention, data processing complexity are high, data feedback is not real-time etc. in the presence of excessively relying on
Problem, but operating cost is low, test is convenient.
Analyze above-mentioned all kinds of attention measurement methods it can be found that general questionnaire scale and working system it is although easy to operate,
It is easy to implement but subjective, and data acquisition and processing (DAP) is inconvenient, time cost is higher；Although and instrument measuring method can
More objective observation method is provided, can evade to a certain extent subject in cognition continuous mode by recall bias,
The influence of Halo effect or other Confounding factors, but this kind of measurement method test wrapper border multipair greatly has higher requirements, and
The equipment prices such as eye tracker, brain wave imager are high, complicated for operation.It is " disposable " note acquired in these measurement methods
Meaning force data, it is difficult to meet the needs of acquiring under current big data environment to data.
The above-mentioned all kinds of attention measurement methods of most critical all have ignored the characteristic of the highly dynamic fluctuation of attention, can only detect
The attention situation of subject in certain circumstances, and subject can not be acquired in real time under pervasive environments such as true classroom
The attention force data of scape.
Summary of the invention
It is an object of the invention to overcome missing and deficiency of the existing technology, a kind of note based on sitting posture analysis is proposed
Meaning power evaluation system and method.
Basic ideas of the invention:
Based on the demonstration about the correlation between sitting posture and attention, it is believed that sitting posture can reflect to a certain extent
The attention situation of learner out.Psychological study shows in the big characteristic of attention four simultaneously, and pays attention stably and learner learn
Apparent correlation is presented in achievement, and the quality quality of pays attention stably plays an important role for learner's learning effect.Accordingly, pass through
To the specificity analysis of learner's Classroom attention, the performance characteristic that attention is highly dynamic and fluctuates is restored, defines and constructs base
In sitting posture learner's attention mapping model and design and develop learner's attention evaluation tool based on sitting posture, and combine fortune
With modern network technologies such as cloud platform technologies, acquisition, storage and the analysis of Classroom attention real time data are realized.
A kind of attention evaluation system based on sitting posture analysis of the present invention, including sitting posture analyze circuit module, and attention is reflected
Module, cloud storage module are penetrated, data analysis and visualization model are connected with data communication method.
The sitting posture analysis circuit module is the acquisition real-time body posture data of user, is analyzed using attitude data current
The chest card of user's sitting posture；
The attention mapping block is based on user's sitting posture and is mapped sitting posture according to the corresponding relationship of sitting posture and attention
To pay attention to force data；
The cloud storage module is using cloud platform as data storage cloud service device, including data upload, data storage, number
According to issuing function；
The data analysis and visualization model analyze the attention force data of processing user according to different dimensions, and with chart
Analysis result is presented in form.
The chest card obtains user's attitude data, is analyzed to obtain user's sitting posture according to attitude data, then according to sitting posture with
Attention corresponding relationship obtains user and pays attention to force data；
The chest card is fixed on the chest locations of user's upper body trunk, includes angular transducer, main process task chip in chest card
And wireless communication module；
The angular transducer establishes XYZ three-dimensional space rectangular coordinate system, judges people according to Roll-Pitch-Yaw model
Swing, left-leaning Right deviation when body sitting posture, right-hand rotation of turning left；
User's attitude data is received by digital communication using main process task chip, is then worked as by the judgement of angle synthesis analysis
Preceding user's sitting posture.
It completes after paying attention to the acquisition and storage of force data, learner classroom is paid attention to by mobile client software (APP)
Power situation carries out real-time monitoring, and from sitting posture frequency statistics figure, attention situation curve graph, same sitting posture continuous time and its distribution figure
(stability of attention) angularly carries out data visualization displaying, completes attention situation analysis suggestion, these data can be real
When big data analysis or post analysis are provided.
In conclusion the present invention is based on the specificity analysis to learner's Classroom attention, reduction attention it is highly dynamic and
The performance characteristic of fluctuation, defines and constructs user's attention mapping model based on sitting posture and the one kind designed and developed is based on sitting posture
The attention evaluation system and method for analysis, and the modern network technologies such as R. concomitans cloud platform technology realize Classroom attention
Acquisition, storage and the analysis big data analysis or post analysis of real time data provide technical guarantee.
Detailed description of the invention
Fig. 1 is a kind of attention evaluation system block diagram based on sitting posture analysis of the present invention；
Fig. 2 is that sitting posture of embodiment of the present invention data are mapped as attention data module connection block diagram；
Fig. 3 is angular transducer of the embodiment of the present invention according to Roll-Pitch-Yaw illustraton of model；
Fig. 4 is that the mobile APP functional module of the embodiment of the present invention connects block diagram.
Specific embodiment
Below in conjunction with drawings and examples, the invention will be further described
A kind of attention evaluation system (as shown in Fig. 1) based on sitting posture analysis, including sitting posture analyze circuit module 1,
Attention mapping block 2, cloud storage module 3, data analysis and visualization model 4 are connected with data communication method.
The sitting posture analysis circuit module 1 acquires the real-time body posture data of user by chest card, utilizes attitude data point
Analyse active user's sitting posture；
The attention mapping block 2 is based on user's sitting posture and is mapped sitting posture according to the corresponding relationship of sitting posture and attention
To pay attention to force data；
The cloud storage module 3 is using data cloud platform as data storage cloud service device, including data upload, data are deposited
Storage, data distributing function；
The data analysis and visualization model 4 analyze the attention force data of processing user according to different dimensions, and to scheme
Analysis result is presented in sheet form.
Sitting posture data of the embodiment of the present invention are mapped as attention data module (as shown in Fig. 2).Wherein, GY-25 is one
Inexpensive gradient module of the money based on MPU-6050 spatial motion sensor chip.GY-25 be on the basis of MPU-6050,
Gyroscope and acceleration transducer data are obtained into final angle-data through data anastomosing algorithm and directly exported by serial ports
To WeMOS-D1 single-chip microcontroller.
Sitting posture data foundation Roll-Pitch-Yaw model (as shown in Fig. 3) that single-chip microcontroller processing GY-25 is sent,
Roll-pitch-yaw model is mainly used for the flight control of unmanned plane or aircraft, and the present invention calculates human body using the model and sits
Appearance situation.It is oriented Y-axis with people back, is oriented X-axis on the right side of body, the crown is oriented Z axis when just sitting, which is applied to human body
It is then that the rotation of human body about the z axis is identified with course angle when sitting posture system, i.e., human body turns left to turn right；Human body is identified with roll angle
Around the rotation of Y-axis, i.e., left-leaning Right deviation；Rotation of the human body around X-axis is identified with pitch angle, that is, is leaned forward and swung back.Determine with course
Footmark is known human body and is rotated about the z axis, turns left to turn right for human body；It is rotated with roll angle mark human body around Y-axis, for left-leaning Right deviation；To bow
Elevation angle mark human body is rotated around X-axis, to lean forward and swinging back.
Then single-chip microcontroller determines active user's sitting posture by angle synthesis analysis by the above processing.And rely on ESP-
8266 chips carry out data encapsulation according to http protocol to data, and connect wireless network, complete hardware by wireless network and set
The standby data to OneNet Cloud Server are transmitted, and realize storage of the data on Cloud Server.Next step data analysis and visual
Change and show, can use the H5 App from research and development, obtain relational learning person's from OneNet Cloud Server by http protocol
Pay attention to force data and sitting posture data, relative recording is checked on App.
Sitting posture attention data module 2 (as shown in Fig. 1) is operate in a set of data processing module in WeMOS-D1.
Different attentions are corresponded to based on different sitting posture types and sitting posture conversion time interval, main process task chip will analyze resulting user and sit
Appearance is converted to corresponding attention score value with attention corresponding relationship according to sitting posture, executes once, to 5 institutes within the process every 1 second
It obtains attention score value and is averaged the attention numerical value as user in this time.
Embodiment, when measure the current roll angle of learner (i.e. human body swing angle) less than -110 degree (experiment measures,
People is just sitting roll angle under range and is being greater than -110 less than -90 degree) when, it can determine whether the current sitting posture of learner for layback sitting posture；Work as detection
To course angle when human body left-right rotation (i.e. angle) varied widely than the course angle of previous moment when, it can be determined that when
Lower learner is in rotary state.After WeMos-D1 preliminary treatment, GY-25, which measures resulting angle-data, to be turned respectively
Be changed to corresponding 9 kinds of sitting postures (just sit, lean forward, lean forward it is to the left, to the right, the left-leaning, Right deviation that leans forward, just swinging back, to the left, layback of swinging back
It is to the right) and 2 kinds turn round.
After obtaining sitting posture data, according to the sitting posture of definition and attention mapping table, sitting posture that WeMos-D1 will be obtained currently
Data are changed into corresponding attention score value.Specific algorithm are as follows: if when previous second learner sitting posture is identical as sitting posture upper one second,
Then indicate that current learner has continued the attention situation of upper one second, attention total score directly adds sitting posture and corresponds to attention at this time
Score value；If learner's sitting posture was changed compared with sitting posture upper one second in this second, mean current learner's attention
In a dispersity, attention total score cannot correspond to score value plus sitting posture.
The attention level condition in learner's time is finally acquired according to this.In the present invention, Wemos-D1 single-chip microcontroller
The GY-25 sensing data (the i.e. each second current sitting posture situation of learner of acquisition) of processing in every 1 second, continuous acquisition five seconds
Interior sitting posture, and this five times sitting posture corresponds to attention score is cumulative to take its average value horizontal as the attention in current 5 seconds
Score uploads cloud database and carries out data storage.
The following are the parsings of 2 mapping table of attention mapping block: comprehensive attention stability, focuses on journey at the attention span
Degree, attentional resources distribution condition index obtain the current attention force data of user.
The sitting posture transformation refers to that previous second sitting posture is different from current second sitting posture；
Sitting posture type includes nine kinds, specifically:
Positive to sit: using body face direction as positive direction, user's body and sitting face are in nearly 90 °, and pre-post difference is no more than 20 ° of seat
Appearance；
Lean forward: using body face direction as positive direction, user's body and sitting face angle are greater than 50 ° less than 70 °；
Before bow: using body face direction as positive direction, user's body and sitting face angle are less than 50 °；
It is left-leaning: when just sitting body trunk upward direction as positive direction, when trunk is deviated to the left positive seat 30 ° of trunk position with
On；
Right deviation: when just sitting body trunk upward direction as positive direction, when trunk is deviated to the right positive seat 30 ° of trunk position with
On；
It leans forward left partial: left-leaning under forward-lean state；
It leans forward right partial: the Right deviation under forward-lean state；
Layback: using body face direction as positive direction, user's body and sitting face angle are greater than 110 °；
Turn round: using body trunk as axis, trunk left-right rotation amplitude is more than 30 °.
The sitting posture includes: with attention corresponding relationship
When user's sitting posture, which is positive, to be sat and lean forward, attention is most concentrated；
When user's sitting posture leans on for the left right side of leaning on and lean forward of leaning forward, attention compares concentration；
When user's sitting posture is left-leaning and Right deviation, attention ratio is less concentrated；
When bowing and swing back before user's sitting posture is, attention laxes；
When user turns round and occur sitting posture transformation, attention most laxes.
The Visualization Platform of data analysis and visualization model 4 is mainstream Android platform or other support HTML5 software
Platform.
The HTML5 plus technology develops IDE by HBuilder, the movement developed using the front end MUI Development Framework
APP realizes that technology essential factor is as follows:
Data downloading: when clicking on mobile App or refresh page generates request of data, mobile App can be utilized user
Ajax () method, by calling jQuery bottom AJAX to the HTTP request of data cloud platform initiation GET, to obtain data；
Data parsing: it after mobile App gets the JSON data packet that data cloud platform issues, is asked after parsing data packet
Seek the attention numerical value in analysis time section in user's each time point；
Data visualization: mobile App utilizes Baidu echarts data visualization drawing interface, the attention that parsing is obtained
Force data is converted into all kinds of charts.
The mobile APP functional module (as shown in Fig. 4), including attention real-time monitoring module and attention force data point
Analyse module；
Wherein,
Attention real-time monitoring module: pass through the two kinds of data visualization mode exhibitions of attention curve graph and attention instrument board
Attention change situation and real-time attentive score in first five minute at moment of current family, interface are refreshed primary for 3 seconds；
Attention data analysis module: mainly showing user's sitting posture type statistics data by Nightingale, Florence rose figure, leads to
It crosses attention change curve graph and shows user's attention change situation, user's note is showed by attention stability change curve graph
Meaning power stability.
In conclusion one kind that the present invention defines and constructs user's attention mapping model based on sitting posture and design and develop
Based on the attention evaluation system and method for sitting posture analysis, by including that sitting posture analyzes circuit module, attention mapping block, cloud
Memory module, data analysis and visualization model are connected with data communication method.Existing all kinds of attention measurement methods are overcome to neglect
The characteristic of the highly dynamic fluctuation of attention has been omited, the attention situation of subject in certain circumstances can only be detected, and can not be real
When acquire user under pervasive environments, the deficiency of attention force data etc. of true classroom scene.Effectively make up existing attention
Evaluation system utilizes the problems such as at high cost, complicated for operation caused by the large-scale monitoring instrument such as eye tracker more.System to user into
The method of row attention assessment includes: the acquisition of user's attitude parameter；Analyze the current sitting posture of user；It is mapped according to sitting posture and attention
Relationship assesses user's attention situation；Attention data cloud storage；Notice that force data is analyzed；It is visual on mobile phone A pp
Change analysis result.It realizes the diversified functions such as the monitoring of user's attention, acquisition, storage, analysis, provides low cost, height for user
Efficiency, small interference attention assessment mode, reduce the highly dynamic fluctuation characteristic of attention, the big data that can be used for imparting knowledge to students receive
Collection.Classroom is realized to provide the acquisition of attention real time data in real time, store and for analysis big data analysis or post analysis
Technical guarantee is provided.
Claims (11)
1. a kind of attention evaluation system based on sitting posture analysis, which is characterized in that analyze circuit module (1) including sitting posture, note
It anticipates power mapping block (2), cloud storage module (3), data analysis and visualization model (4) are connected with data communication method.
2. a kind of attention evaluation system based on sitting posture analysis as described in claim 1, which is characterized in that the sitting posture point
Analysing circuit module (1) is the acquisition real-time body posture data of user, utilizes the chest card of attitude data analysis active user's sitting posture；
The attention mapping block (2) is based on user's sitting posture and is mapped as sitting posture according to the corresponding relationship of sitting posture and attention
Pay attention to force data；
The cloud storage module (3) is using cloud platform as data storage cloud service device, including data upload, data storage, number
According to issuing function；
The data analysis and visualization model (4) analyze the attention force data of processing user according to different dimensions, and with chart
Analysis result is presented in form.
3. a kind of attention evaluation system based on sitting posture analysis as claimed in claim 2, which is characterized in that the chest card obtains
Family attitude data is taken, is analyzed to obtain user's sitting posture according to attitude data, then be obtained according to sitting posture and attention corresponding relationship
User pays attention to force data；
The chest card is fixed on the chest locations of user's upper body trunk or is placed on cap or the helmet, includes that angle passes in chest card
Sensor, main process task chip and wireless communication module；
The angular transducer establishes XYZ three-dimensional space rectangular coordinate system according to Roll-Pitch-Yaw model, judges that human body is sat
Swing, left-leaning Right deviation when appearance, right-hand rotation of turning left；
User's attitude data is received by digital communication using main process task chip, current use then is determined by angle synthesis analysis
Family sitting posture.
4. a kind of attention evaluation system based on sitting posture analysis as claimed in claim 1 or 2, which is characterized in that the note
It anticipates power mapping block (2), corresponds to different attentions based on different sitting posture types and sitting posture conversion time interval, pass through main process task core
Piece will analyze resulting user's sitting posture and be converted to corresponding attention score value with attention corresponding relationship according to sitting posture.
5. a kind of attention evaluation system based on sitting posture analysis as claimed in claim 1 or 2, which is characterized in that the cloud
The data of memory module (3) upload: handling after resulting user notices that force data is packaged by main process task chip, by wireless communication
Module is uploaded to cloud platform；
Data storage: after cloud platform receives the attention force data uploaded, parsing the data of upload, raw according to data uplink time
At user's attention data point, data point records the time and attention score value that the user notices that force data uploads；
Data distributing: the data in the requirement analysis period are packaged by cloud platform according to Client-initiated data analysis request
JSON data format, is issued by http protocol.
6. the attention evaluation system as claimed in claim 1 or 2 based on sitting posture analysis, which is characterized in that the data point
The Visualization Platform of analysis and visualization model (4) is mainstream Android platform or other support HTML5 software platform.
7. a kind of attention evaluation system based on sitting posture analysis as claimed in claim 6, which is characterized in that the HTML5
Plus technology, develops IDE by HBuilder, and the mobile APP developed using the front end MUI Development Framework realizes technology essential factor such as
Under:
Data downloading: for user when clicking on mobile App or refresh page generates request of data, mobile App can utilize Ajax ()
Method, by calling jQuery bottom AJAX to the HTTP request of cloud platform initiation GET, to obtain data；
Data parsing: after mobile App gets the JSON data packet that cloud platform issues, when obtaining requirement analysis after parsing data packet
Between attention numerical value in section in user's each time point；
Data visualization: mobile App utilizes Baidu echarts data visualization drawing interface, the attention number that parsing is obtained
According to being converted into all kinds of charts.
8. a kind of attention evaluation system based on sitting posture analysis as claimed in claim 7, which is characterized in that the movement
APP includes attention real-time monitoring module and attention data analysis module；
Wherein,
Attention real-time monitoring module: by two kinds of data visualization modes of curve graph and instrument board show user's moment first five
Attention change situation and real-time attentive score in minute, interface are refreshed primary for 3 seconds；
Attention data analysis module: user's sitting posture type statistics data are mainly showed by Nightingale, Florence rose figure, pass through song
Line chart shows user's attention change situation, shows user's attention stability by difference curve figure.
9. a kind of attention assessment method based on sitting posture analysis as described in claim 1, which is characterized in that using sitting posture as
Aobvious observational variable outside attention situation grasps user's attention situation according to sitting posture and attention corresponding relationship, specific to test and assess
Method are as follows:
The current attention stability of user is demarcated according to the sitting posture number of transitions of user；
The current attention span of user is demarcated according to the sitting posture type of user, focuses on degree and attentional resources distribution feelings
Condition；
Comprehensive attention stability, the attention span, focus on degree, that attentional resources distribution condition index obtains user is current
Pay attention to force data.
10. a kind of attention assessment method based on sitting posture analysis as claimed in claim 9, which is characterized in that the sitting posture
Transformation refers to that previous moment sitting posture is different from current time sitting posture；Time at intervals can be adjusted as needed；
Sitting posture type includes nine kinds, specifically:
Positive to sit: using body face direction as positive direction, user's body and sitting face are in nearly 90 °, and pre-post difference is no more than 20 ° of sitting posture；
Lean forward: using body face direction as positive direction, user's body and sitting face angle are greater than 50 ° less than 70 °；
Before bow: using body face direction as positive direction, user's body and sitting face angle are less than 50 °；
Left-leaning: body trunk upward direction is positive direction when just sitting, 30 ° of trunk position or more when trunk is deviated to the left positive seat；
Right deviation: body trunk upward direction is positive direction when just sitting, 30 ° of trunk position or more when trunk is deviated to the right positive seat；
It leans forward left partial: left-leaning under forward-lean state；
It leans forward right partial: the Right deviation under forward-lean state；
Layback: using body face direction as positive direction, user's body and sitting face angle are greater than 110 °；
Turn round: using body trunk as axis, trunk left-right rotation amplitude is more than 30 °.
11. a kind of attention assessment method based on sitting posture analysis as claimed in claim 9, which is characterized in that the sitting posture
Include: with attention corresponding relationship
When user's sitting posture, which is positive, to be sat and lean forward, attention is most concentrated；
When user's sitting posture leans on for the left right side of leaning on and lean forward of leaning forward, attention compares concentration；
When user's sitting posture is left-leaning and Right deviation, attention ratio is less concentrated；
When bowing and swing back before user's sitting posture is, attention laxes；
When user turns round and occur sitting posture transformation, attention most laxes.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109885167A (en) * | 2019-02-25 | 2019-06-14 | 北京七鑫易维信息技术有限公司 | Data processing method, data transmission method and device |
| CN111339801A (en) * | 2018-12-19 | 2020-06-26 | 杭州海康威视系统技术有限公司 | Method, device, equipment and system for detecting attention of people |
| CN112235465A (en) * | 2020-10-27 | 2021-01-15 | 四川金沐志德科技有限公司 | Learning task and financing management system based on intelligent terminal |
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| CN113656122A (en) * | 2021-07-28 | 2021-11-16 | 上海纽盾科技股份有限公司 | Information screening method, device and system for equal protection evaluation |
| CN114159077A (en) * | 2022-02-09 | 2022-03-11 | 浙江强脑科技有限公司 | Meditation scoring method, device, terminal and storage medium based on electroencephalogram signals |
| CN115250379A (en) * | 2021-04-25 | 2022-10-28 | 花瓣云科技有限公司 | Video display method, terminal, system and computer readable storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1496522A (en) * | 2000-03-29 | 2004-05-12 | �ʼҷ����ֵ�������˾ | Data serch user interface with ergonomic mechanism for user profile definition and manipulation |
| CN202191302U (en) * | 2011-04-29 | 2012-04-18 | 无锡万寿物联网科技有限公司 | Study and office efficiency measurer |
| CN203378785U (en) * | 2013-06-09 | 2014-01-08 | 陶国慧 | Ergonomic medical health-care chair |
| CN103519788A (en) * | 2013-10-18 | 2014-01-22 | 南京师范大学 | Attention scenario evaluation system based on Kinect interaction |
| CN104905584A (en) * | 2015-06-25 | 2015-09-16 | 苏州安莱光电科技有限公司 | Non-invasive intelligent cushion for monitoring sitting posture |
| CN107293099A (en) * | 2017-07-04 | 2017-10-24 | 歌尔科技有限公司 | A kind of attitude monitoring method, device and wearable device |
| US20180075293A1 (en) * | 2016-09-15 | 2018-03-15 | Georgios P. Schinas | Innovative anti-bullying approach using emotional and behavioral information from mixed worlds |
-
2018
- 2018-05-03 CN CN201810413334.XA patent/CN108937965B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1496522A (en) * | 2000-03-29 | 2004-05-12 | �ʼҷ����ֵ�������˾ | Data serch user interface with ergonomic mechanism for user profile definition and manipulation |
| CN202191302U (en) * | 2011-04-29 | 2012-04-18 | 无锡万寿物联网科技有限公司 | Study and office efficiency measurer |
| CN203378785U (en) * | 2013-06-09 | 2014-01-08 | 陶国慧 | Ergonomic medical health-care chair |
| CN103519788A (en) * | 2013-10-18 | 2014-01-22 | 南京师范大学 | Attention scenario evaluation system based on Kinect interaction |
| CN104905584A (en) * | 2015-06-25 | 2015-09-16 | 苏州安莱光电科技有限公司 | Non-invasive intelligent cushion for monitoring sitting posture |
| US20180075293A1 (en) * | 2016-09-15 | 2018-03-15 | Georgios P. Schinas | Innovative anti-bullying approach using emotional and behavioral information from mixed worlds |
| CN107293099A (en) * | 2017-07-04 | 2017-10-24 | 歌尔科技有限公司 | A kind of attitude monitoring method, device and wearable device |
Non-Patent Citations (1)
| Title |
|---|
| 李卉等: "基于Kinect 体感交互的儿童注意力测评系统", 《XDMS》 * |
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