CN109199364A - Application based on cardiac electrical focus curve generation method and segmentation instructional video - Google Patents
Application based on cardiac electrical focus curve generation method and segmentation instructional video Download PDFInfo
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- CN109199364A CN109199364A CN201811154057.1A CN201811154057A CN109199364A CN 109199364 A CN109199364 A CN 109199364A CN 201811154057 A CN201811154057 A CN 201811154057A CN 109199364 A CN109199364 A CN 109199364A
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- 239000013598 vector Substances 0.000 claims abstract description 10
- 238000012549 training Methods 0.000 claims abstract description 8
- 238000011946 reduction process Methods 0.000 claims abstract description 7
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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
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
- H04N21/845—Structuring of content, e.g. decomposing content into time segments
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Abstract
The invention discloses a kind of based on cardiac electrical focus curve generation method and divides the application of instructional video, advantage is to have filtered out noise and abnormal signal by noise reduction process after the electrocardiosignal of acquisition is converted into digital signal, improves the accuracy of time-frequency data after data transformation；Then time-frequency data are converted to by normalized focus score data by the support vector regression that data training obtains, focus curve is obtained according to focus score data, it is quickly glided according to continuity certain time high focus, focus in the focus curve of generation or the zooming period divides the instructional video editing that instructional video obtains the period, to which accurate perception break is tested the focus and situation of change of student, form the effective Feedback of intercommunication education of school, for analytic instruction video, improves classroom instruction and strong auxiliary is provided.
Description
Technical field
The present invention relates to the teaching research technical fields based on biofeedback, are absorbed in more particularly, to one kind based on cardiac electrical
Line generation method of writing music and the application for dividing instructional video.
Background technique
In daily education activities, the teaching quality evaluation of lecturer is the key link of quality of instruction assessment, to raising
Quality of instruction and office efficiency are of great significance, and therefore, only establishing perfect quality of instruction evaluation system can be more
The teaching of comprehensive and accurate evaluation lecturer, improves teaching level comprehensively.It is main using objective in conventional teaching evaluation
Examination result analysis, and the mode of more subjective student's questionnaire marking and informal discussion；This teaching evaluation reaction is overall
Implementations, the case where specific certain class cannot be reacted.And actual, the focus and situation of change of specific class student every time
How, the grasp and analysis of these data can be constituted more efficiently auxiliary with the raising of intercommunication education of school.
Summary of the invention
A technical problem to be solved by this invention is to provide a kind of based on cardiac electrical focus curve generation method, use
In the focus and situation of change of grasping and analyze break test student.
Technical solution used by this focus curve generation method are as follows: one kind is based on cardiac electrical focus curve generation side
Method, specifically includes the following steps: S1 acquires electrocardiosignal；Electrocardiosignal is converted into digital signal and is transferred to data processing by S2
Unit；S3 digital signal obtains time-frequency data after noise reduction process, through time frequency analysis；The support that S4 is obtained according to data training
Time-frequency data are converted to normalized focus score data by vector regression；S5 is absorbed according to focus score data
It writes music line.
The acquisition of step S1 center telecommunications number uses PulseSensor sensor, HKD-10b EGC sensor, BMD101
The one of which of cardioelectric monitor sensor or AD8232 heart rate monitor sensor series.
Electrocardiosignal is converted by digital signal using Arduino single-chip microcontroller in step S2.
Noise reduction process in step S3 specifically: first pass through low-pass filter and filter out noise, then filtered by Threshold Analysis
Abnormal signal.
In step S3, time frequency analysis uses short time discrete Fourier transform or continuous wavelet transform.
Compared with prior art, it advantage of this approach is that before electrocardiosignal is transformed into time-frequency data, is being converted into counting
Noise and abnormal signal have been filtered out by noise reduction process after word signal, improved the accuracy of data；Then pass through data training
Time-frequency data are converted to normalized focus score data by obtained support vector regression, further according to focus score value number
According to focus curve is obtained, so as to the focus and situation of change of accurate perception break test student, formed to lecturer
The effective Feedback of teaching is analytic instruction video, improves classroom instruction and provides strong auxiliary.
Another technical problem to be solved by this invention is to provide a kind of answering using focus curve segmentation instructional video
With.
Technical solution used by this application are as follows: a kind of application using focus curve segmentation instructional video, by above-mentioned
Continue certain time high focus, focus in the focus curve that method generates quickly to glide or the zooming time
Section obtains the instructional video editing of the period in instructional video.
Compared with prior art, be the advantages of this application according to continue in focus curve long period high focus or
Person's fast changing portion obtains the corresponding instructional video editing of the period in instructional video, analyzes this section of time religion for lecturer
The reason of learning variation, provides strong auxiliary for the improvement of classroom instruction.
Detailed description of the invention
Fig. 1 is that this preferred embodiment is based on cardiac electrical focus curve generation method and is imparted knowledge to students using focus curve segmentation
The step schematic diagram of the application of video.
Specific embodiment
The present invention will be described in further detail below with reference to the embodiments of the drawings.
The present embodiment discloses one kind based on cardiac electrical focus curve generation method as shown in Figure 1 and is write music using being absorbed in
Line divides the application of instructional video, and specific steps include:
The electrocardiosignal of S1 collecting test student, while the teaching that the video recorder record lecturer being equipped with classroom attends class regards
Frequently；In this preferred embodiment, electrocardiosignal is acquired using PulseSensor sensor, PulseSensor sensor passes through photoelectricity
Reflection acquisition electrocardiosignal, collected electrocardiosignal are analog signal；Wherein, PulseSensor sensor is matched by annulus
It wears on the arm that test student does not hold a pen, PulseSensor sensor is fixed on above the arm blood vessel；Intermediate plate can also be passed through
Clamping finger is located in PulseSensor sensor above finger capillary.Herein, PulseSensor sensor
Other optics heart rate sensors or pulse transducer substitution can be used, as HKD-10b EGC sensor, BMD101 cardioelectric monitor pass
Sensor and AD8232 heart rate monitor sensor series.
Collected electrocardiosignal is converted into digital signal by S2, is then transferred to data processing by wireless communication module
Unit.In this preferred embodiment, the conversion of AD signal is carried out using Arduino single-chip microcontroller, Arduino is a convenient flexible, side
Just the open source electronics Prototyping Platform of upper hand, the program code that Arduino IDE writes can directly be passed to Arduino single-chip microcontroller with
Control the conversion of AD signal；Wherein, PulseSensor sensor is connect by conducting wire with Arduino single-chip microcontroller；Wireless communication module
Using Freescale intelligent radio module, it is mono- that Freescale intelligent radio module by 433M wireless serial is connected to Arduino
On piece machine.Herein, electrocardial analog signal can also can be converted into digital signal, company by Arduino single-chip microcontroller with other
The processor replacement for connecing wireless communication module, such as ARM11 series；Wireless communication module can be used 490M, 780M, 868M, 915M,
The replacement of 2.4G series wireless sensor module.
For S3 at data processing unit, digital signal obtains time-frequency data after noise reduction process, through time frequency analysis；Specifically
, digital signal first passes through low-pass filter and filters out noise, then filters peak value not in the different of normal range (NR) by Threshold Analysis
Regular signal, then pass through short time discrete Fourier transform or continuous wavelet transform for digital signal conversion into time-frequency data；Wherein, abnormal letter
Number generation be usually by student adjust sitting posture when shake arm cause so that PulseSensor sensor acquisition electrocardio letter
It is number not normal.
Time-frequency data are converted to normalized focus score value according to the support vector regression that data training obtains by S4
Data.
Wherein, data training process is as follows:
It is prediction student that a1, which chooses each 5 people of male and female students, and applied mathematics calculation, the modes such as meditation or film viewing induce pre-
Survey the focus variation of student.
The mindband eeg collection system of a2 application NeuroSky company carries out brain wave acquisition, and applies the mindband
The software kit system of eeg collection system carries out focus calculating, record corresponding focus y when predicting student i-th secondi(i
=1 ..., n), yi∈ (0,1), n are the record number of seconds for predicting that student is total.
A3 carries out electrocardiogram acquisition using PulseSensor with while brain wave acquisition, obtains the electrocardiogram (ECG) data of corresponding time
dsi(i=1 ..., n)；
A4 is to dsiCarry out time frequency analysis, obtain time-frequency data, i.e., with 0.1~3Hz of frequency domain, 4~7Hz, 8~12Hz, 13~
The corresponding normalized energy vector x s of 15Hz, 16~20Hz, 21~30Hzi, xsiFor 6 dimensional vectors.
A5 is supported vector regression training using following formula (1), i.e., optimizes to formula (1), obtains specially
Note degree anticipation function f:
Wherein, loss () is loss function, f (xsi) it is the prediction student i-th being calculated by focus anticipation function f
Focus score data when the second, λ ∈ [0.1,10] are complexity control parameter,Be focus anticipation function f Europe it is several
In norm square.
Test student jth (j=1 ..., n) on classroom is acquired using the obtained focus anticipation function f of data training
The focus score data f (xt of secondj)(f(xtj)∈(0,1))；Wherein, time-frequency data xtjFor 6 dimensional vectors, xtjRefer to dtjWhen
Obtained after frequency analysis with 0.1~3Hz of frequency domain, 4~7Hz, 8~12Hz, 13~15Hz, 16~20Hz, 21~30Hz is corresponding
Normalized energy vector；dtjFor the electrocardiogram (ECG) data for collecting test student's jth second in classroom using PulseSensor.
Herein, the relationship of electrocardio frequency domain and focus is referring to the following table 1:
Electrocardio frequency domain and focus relation table 1:
S5 obtains focus curve according to focus score data；Wherein, focus curve refers to focus score data
Change over time the curve to be formed；
S6 quickly glides or quick according to continuing certain time high focus, focus in the focus curve of generation
The period segmentation instructional video of rising obtains the instructional video editing of the period.The period that focus quickly glides refers to
Curve suddenly falls to the gradually stable period in focus curve, and the focus zooming period, which refers to be absorbed in, writes music
Curve sharp rises to the gradually stable period in line；In addition, continuity certain time reaches 3~10 in focus curve
Minute high focus (focus score data is 0.6 or more), indicate that student can clearly understand the content of courses of lecturer.And
Variation in focus curve be often as student because instructional blocks of time length relationship certain periods generate it is tired,
Gradually regain consciousness or situations such as content of courses is difficult to understand for caused variation.
In the present embodiment, the when frequency division of the noise reduction process of digital signal, short time discrete Fourier transform or continuous wavelet transform
Analysis is handled in the MATLAB software of data processing unit, the electricity that data processing unit generally uses arithmetic speed fast
Brain or minicomputer.
Claims (6)
1. one kind is based on cardiac electrical focus curve generation method, which is characterized in that specifically includes the following steps:
S1 acquires electrocardiosignal；
Electrocardiosignal is converted into digital signal and is transferred to data processing unit by S2；
S3 digital signal obtains time-frequency data after noise reduction process, through time frequency analysis；
Time-frequency data are converted to normalized focus score data according to the support vector regression that data training obtains by S4；
S5 obtains focus curve according to focus score data.
2. according to claim 1 be based on cardiac electrical focus curve generation method, it is characterised in that: electrocardio in step S1
The acquisition of signal using PulseSensor sensor, HKD-10b EGC sensor, BMD101 cardioelectric monitor sensor or
The one of which of AD8232 heart rate monitor sensor series.
3. according to claim 1 be based on cardiac electrical focus curve generation method, it is characterised in that: used in step S2
Electrocardiosignal is converted into digital signal by Arduino single-chip microcontroller.
4. according to claim 1 be based on cardiac electrical focus curve generation method, it is characterised in that: noise reduction in step S3
Processing specifically: first pass through low-pass filter and filter out noise, then pass through Threshold Analysis Exception Filter signal.
5. according to claim 1 be based on cardiac electrical focus curve generation method, it is characterised in that: in step S3, when
Frequency analysis uses short time discrete Fourier transform or continuous wavelet transform.
6. a kind of application using focus curve segmentation instructional video, it is characterised in that: generated by Claims 1 to 5 special
Note, which is write music, continues that certain time high focus, focus quickly glide or the zooming period obtains teaching and regards in line
The instructional video editing of the period in frequency.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113641241A (en) * | 2021-08-10 | 2021-11-12 | 西安领跑网络传媒科技股份有限公司 | Concentration degree-based classroom auxiliary teaching system, equipment and method |
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