CN110353686A - A kind of Tai Ji tutor auxiliary platform equipment based on breathing detection - Google Patents
A kind of Tai Ji tutor auxiliary platform equipment based on breathing detection Download PDFInfo
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- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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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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Abstract
The Tai Ji tutor auxiliary platform equipment based on breathing detection that the invention discloses a kind of, including sound transducer, respiratory monitor and communication unit, wherein: sound transducer is used to acquire breathing acoustical signal when people's movement, and analog voltage signal is converted by the breathing acoustical signal, which is transmitted to respiratory monitor by communication unit；Respiratory monitor includes signal processing module and signal display module, wherein signal processing module carries out noise reduction process to analog voltage signal, i.e., is classified according to setting classification thresholds to analog voltage signal, be divided into expiration type and aspirating atmospheric type；Respiratory rate and breathing accounting and exercise heart rate are calculated according to classification results simultaneously；Signal display module is used to show respiratory rate and breathing accounting and exercise heart rate.The Tai Ji tutor auxiliary platform equipment can accurately and efficiently realize the classification of breathing when to progress Taiji sports, which can assist Tai Ji to impart knowledge to students.
Description
Technical field
The invention belongs to teaching equipment fields, and in particular to a kind of Tai Ji tutor auxiliary platform equipment based on breathing detection.
Background technique
Arduino is a convenient flexible, hand in convenience open source electronics Prototyping Platform.Comprising hardware (various models
Arduino plate) and software (Arduino IDE).It is developed by an European development teams in winter in 2005.It is implemented in opening
Source code simple I/O interface version, and have and develop environment using the Processing/Wiring of similar Java, C language.
Main includes two parts: hardware components can be used to do the Arduino circuit board of circuit connection；Another is then
Arduino IDE, the programming development environment in computer.It can be used to many electronic productions with Practical significance of production
Product, such as electronic clock, 3D printer etc..Currently, deep learning be widely used in target detection, image detection,
The fields such as data generation.It is also significantly increased by the accuracy that depth model carries out Classification and Identification to picture or signal.
Application publication number discloses a kind of sleep breath monitoring method for the patent application of CN108420408A, including as follows
Step: Step 1: average suction duration and average expiration duration are calculated, Step 2: determining to enter apnea stage, step
Three, pulse parameter is set, Step 4: after issuing the first subpulse, the variation of ventilator detection flows, Step 5: comparing flow
Waveform, and record the duration of obstructive type apnea and current number of pulses size, obstructive type apnea monitoring and terminate.
The detection breath method is based only upon breathing length to detect, not smart enough.
Summary of the invention
The Tai Ji tutor auxiliary platform equipment based on breathing detection that the present invention provides a kind of, which can
Accurately and efficiently realize the classification of breathing when to progress Taiji sports, which can assist Tai Ji to impart knowledge to students, to mention
The Accuracy and high efficiency of high Tai Ji teaching process.
The technical solution of the present invention is as follows:
A kind of Tai Ji tutor auxiliary platform equipment based on breathing detection, including sound transducer, respiratory monitor and be used for
Realize the communication unit that sound transducer is communicated with respiratory monitor, in which:
Sound transducer is used to acquire breathing acoustical signal when people's movement, and converts analog voltage for the breathing acoustical signal
Signal, the analog voltage signal are transmitted to respiratory monitor by communication unit；
Respiratory monitor includes signal processing module and signal display module, wherein signal processing module is to simulation electricity
It presses signal to carry out noise reduction process, i.e., is classified according to setting classification thresholds to analog voltage signal, be divided into expiration type and suction
Gas type；Respiratory rate and breathing accounting and exercise heart rate are calculated according to classification results simultaneously；Signal display module for pair
Respiratory rate and breathing accounting and exercise heart rate are shown.
The Tai Ji tutor auxiliary platform equipment can according to the voice signal of acquisition calculate output respiratory rate and breathing accounting with
And exercise heart rate, the respiratory rate and breathing accounting and exercise heart rate can assist Tai Ji to impart knowledge to students.
Preferably, in signal processing module, the process of noise reduction process is carried out to analog voltage signal are as follows:
Classification thresholds are set as 400, the analog voltage signal that will be above 400 is defined as corresponding exhalation process, exports 1 table
The high level shown；It is defined as corresponding breathing process less than the analog voltage signal higher than 400, exports the low level indicated with 0, it should
High level and low level shown by waveform diagram, that is, the respiratory waveform figure after forming noise reduction.
After obtaining the respiratory waveform figure after noise reduction, turned in air-breathing to exhaling according to respiratory waveform figure according to time sequencing
Alternatively border records current time t1；It exhales in next time and arrives air-breathing Turning Point, record current time t2；In next air-breathing to exhaling
When gas shift, current time t3 is recorded；Then t3-t1 is 1 breathing time, obtains respiratory rate with this；
T2-t1 is 1 expiratory duration, and t3-t2 is 1 inspiratory duration, obtains tidal air accounting with this；
Exercise heart rate can be obtained according to respiratory rate and the calculating of tidal air accounting.
In order to realize that the Accurate classification to analog voltage signal, the respiratory monitor further include for realizing to simulation electricity
The breathing classifier that pressure signal is classified automatically；
The breathing classifier, which is that the neural network to be made of LSTM unit, convolutional layer and full articulamentum is trained, to be obtained
It arrives；After analog voltage signal converts breath signal matrix according to a certain period of time, after being input to breathing classifier, it is computed,
Export the corresponding type of respiration of analog voltage signal in the time cycle.
In order to handle analog voltage signal using neural network, breath signal matrix is being converted by analog voltage signal
When, the size of breath signal matrix is limited as [2n, 1], wherein 2nN times of expression 2, the expression specimen sample time cycle, i.e., one
A time cycle includes 2nA sampling time point, 1 indicates the corresponding analog voltage signal value of sampling time point.
Specifically, breathing classifier successively includes input layer, LSTM unit, the first full connection according to the sequence of data flow
Layer, the second full articulamentum, the convolutional layer that continuous 7 matrix sizes successively decrease, port number is incremental, the full articulamentum of third and output
Layer.And the breathing classification results for breathing classifier output are shown in the form of waveform diagram in signal display module.
Compared with prior art, the Tai Ji tutor auxiliary platform equipment provided by the invention based on breathing detection can be realized to adopting
The analog voltage signal of collection is classified automatically, and classification results are converted into the signal waveforms of digital visual, with auxiliary
Tai Ji teaching.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to do simply to introduce, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art, can be with root under the premise of not making the creative labor
Other accompanying drawings are obtained according to these attached drawings.
Fig. 1 is the original human body breath signal waveform diagram that respiration transducer captures；
Fig. 2 is the human body respiration waveform diagram after noise reduction process；
Fig. 3 is the structural schematic diagram for breathing classifier；
Fig. 4 is to the corresponding classification results schematic diagram of breath signal.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention more comprehensible, with reference to the accompanying drawings and embodiments to this
Invention is described in further detail.It should be appreciated that the specific embodiments described herein are only used to explain the present invention,
And the scope of protection of the present invention is not limited.
For the high efficiency and accuracy for improving Tai Ji teaching process and other sports teaching, present embodiments provide
A kind of Tai Ji tutor auxiliary platform equipment of breathing detection, the Tai Ji tutor auxiliary platform equipment include sound transducer, respiratory monitor with
And the communication unit communicated for realizing sound transducer with respiratory monitor.
Wherein, sound transducer is used to acquire breathing acoustical signal when people moves, and converts mould for the breathing acoustical signal
Quasi- voltage signal, the analog voltage signal are transmitted to respiratory monitor by communication unit (can be bluetooth module)；
Respiratory monitor includes signal processing module and signal display module, wherein signal processing module is to simulation electricity
It presses signal to carry out noise reduction process, i.e., is classified according to setting classification thresholds to analog voltage signal, be divided into expiration type and suction
Gas type；Respiratory rate and breathing accounting and exercise heart rate are calculated according to classification results simultaneously；Signal display module for pair
Respiratory rate and breathing accounting and exercise heart rate are shown.
Sound transducer can be the built-in one condenser type Electret condenser microphone to sound sensitive.Sound wave makes staying in microphone
Polar body vibration of thin membrane leads to the variation of capacitor, and generates the small voltage for variation of being corresponding to it.This voltage is subsequently converted into
The voltage of 0-5V is converted by A/D and is received by data collector.
Serial communication is established by Python, pyserial module encapsulates the access to serial ports, is Linux, Windows
It is run in operating system and IronPython etc. all compatible POSIX (portable operating system interface) and rear end is provided.It is entitled
The module of " serial " can automatically select suitable rear end.Under a windows environment, it can be determined and be gone here and there by resource manager
Mouthful, the relevant variable of a serial ports is resettled, determines bluetooth serial ports and communication baud rate.
Sound transducer and bluetooth module are connected to Arduino plate properly, and breathing prison is connected to by USB
It surveys device (can be a small-sized computer).Under 10 environment of Windows, driving is installed by system automatically substantially.Pass through again
Arduino IDE edit code is uploaded to Arduino plate, the information such as baud rate of sound transducer signal is arranged and by bluetooth mould
Block is set as host mode, and the voice signal that sound transducer obtains is transmitted to computer by bluetooth serial ports communication.
After respiratory monitor obtains people's breath signal, visualized operation is carried out to obtained original breath digital signal,
Enable a user to the motion conditions of preferably observation sporter in Tai Ji education activities.Use Python's
Matplotlib packet generates good signal waveforms.The value array an of appropriate length is established according to needed for image simultaneously
Initialization value is 0, and array can obtain the analog voltage signal of serial communication, determines that X-axis is time, Y by Matplotlib
Axis is analog voltage, and the signal data that array stores can be determined to suitable Y-axis amplitude carries out DYNAMIC DRAWING and shows in real time
(Dynamic graphic display in the data of similar Matlab), one data of every acquisition automatically delete array a data, and will
A data is placed on prior location afterwards, space is created for new data, to not cause memory to overflow.So as to obtain as
The original waveform figure of attached drawing 1.There are more burr and noises for waveform diagram as can be seen from Fig., and first the progress first step, which is crossed, filters out
It makes an uproar.Being obtained by research can define when longitudinal axis value is greater than 400 as people's exhalation process, and may be defined as breathing process less than 400.
So threshold value 400 is arranged in code, when value value is 400 or more, output is 1 high level, and on the contrary then output is 0 i.e. low
Level.
Situations such as presence breathing dies down in the process due to obtaining breath signal in use, external interference, can be set array,
To save several data-signals (according to circumstances taking the circumstances into consideration to judge) of the data-signal obtained instantly and acquisition before, when signal
When value and the excessive data fluctuations of preservation before, then the front signal output of holding is still selected, and will be protected in current data
There are in array, and according to the value of the signal obtained later to determine whether for breathing state conversion and caused by data
Larger situation is fluctuated, if so, the output of change signal.By can get after the filtering noise reduction of the first step such as 2 institute of attached drawing
The waveform diagram shown.
By the breath signal of acquisition, we can calculate heart rate when people's movement.Exercise heart rate, i.e. human body are moving
When the Heart Rate States that keep.Either aerobic exercise or anaerobic exercise.There is a suitable heart rate to can be only achieved preferably
Movement effects.Keep optimal movement heart rate all critically important for movement effects and sports safety.The array that length is 3 is set,
For image by air-breathing to expiration Turning Point, present system time t1 is recorded in setting, then to next breathing conversion, record the
Two system time t2 record third system time t3 after third time breathes gas shift.When t3-t1 is respiration
Between, this makes it possible to obtain respiratory rates, and similarly t2-t1 is expiratory duration, and t3-t2 is inspiratory duration, and acquisition expiration is accounted for air-breathing
Than.The exercise heart rate that people is obtained according to the respiratory rate of calculating and tidal air accounting, preferably can assist Tai Ji to impart knowledge to students
Journey and other movement teaching process.
To improve the accuracy exhaled with air-breathing respectively, which further includes one for exhaling original
Inhale the breathing classifier that signal is divided into two class signal of expiration and air-breathing.
Breathing classifier is mainly used for the classification to breath signal.Classifier is breathed by LSTM unit, convolutional layer and Quan Lian
Connect the neural network of layer composition, structure is as shown in Figure 3, comprising: the breath signal being originally inputted having a size of [512,1], wherein
512 indicate the sampling time point of breath signal, and 1 indicates characteristic value (the namely analog voltage of breath signal each time point
Value), training process using the training of most small quantities of gradient descent method, it is most small quantities of in per a batch of respiratory signal data number of samples
Generally be taken as 64, the characteristic layer having a size of [512,128] obtained after LSTM unit, wherein 512 it is corresponding with it is original when
Between point, the feature vector that 128 corresponding each time points are calculated obtains the characteristic layer having a size of 128 using full articulamentum；Make
The characteristic layer having a size of 128 is obtained with full connection；The characteristic layer having a size of 64*64*3 is obtained using full articulamentum；It uses
Reshape function deforms characteristic layer to obtain the characteristic layer of [64,64,3], wherein 64 respectively correspond characteristic layer length and
Width, the depth of 3 character pair layers；The maximum pond for being 2 using the convolution module having a size of [5,5,64] and having a size of [2,2] step-length
Change module and obtains the characteristic layer having a size of [32,32,64]；It uses the convolution module of [5,5,128] and is having a size of [2,2] step-length
2 maximum pond module obtains the characteristic layer having a size of [16,16,128]；Using having a size of [5,5,256] convolution module and
The maximum pond module for being 2 having a size of [2,2] step-length obtains the characteristic layer having a size of [8,8,256]；Using having a size of [5,5,
512] convolution module and the maximum pond module for being 2 having a size of [2,2] step-length obtain the characteristic layer having a size of [4,4,512]；
Using having a size of [5,5,512] convolution module and having a size of [2,2] step-length be 2 maximum pond module obtain having a size of [2,
2,512] characteristic layer；The maximum Chi Huamo for being 2 using the convolution module having a size of [5,5,512] and having a size of [2,2] step-length
Block obtains the characteristic layer having a size of [1,1,512]；By reshape function and full articulamentum obtain having a size of 2 characteristic layer most
For output (for comprising exhale and two kinds of signal types of air-breathing classification task), obtained not normalized confidence value with
The true corresponding category of breath signal does cross entropy and calculates distance.
The design object of above-mentioned breathing classifier training system are as follows:
Classified automatically to original breath signal by breathing classifier, input sample of the breath signal as network
This, through prediction category at a distance from true category, to adjust the parameter inside breathing classifier.
Specifically, above-mentioned model training system detailed process are as follows:
The epochs=N1 of training is set, i.e. training set is used N1 times.The input of Modulation recognition net is original breath letter
Number, export the category prediction for corresponding breath signal.
Specifically, original signal data collection X is inputted, X is input in disaggregated model, 20 epochs of training.
Specific experiment:
Data set basic condition includes: that (a) mutual trust signal data has 312000 training samples and 156000 test specimens
This, each sample-size is the matrix of 512*1.Verifying collection is the sample size for extracting 30% from test sample at random；(b) number
Two classes can be divided into according to modulation type according to collection, every class equal part, every class has 109200 samples, every class in test set in training set
There are 46800 samples.
Model training system of the above-mentioned training set to above-mentioned building is trained, trained breathing classifier is obtained.
And the sample in test set is input in breathing classifier, the corresponding classification results of 2 class breath signals as shown in Figure 4, this
A little signals are manually difficult entirely accurate and distinguish expiration or air-breathing, illustrate that breathing classifier reaches the classification of breath signal
Expected effect.Therefore, breathing when can accurately and efficiently be realized using the Tai Ji tutor auxiliary platform equipment to progress Taiji sports
Classification, which can assist Tai Ji to impart knowledge to students, to improve the Accuracy and high efficiency of Tai Ji teaching process.
Technical solution of the present invention and beneficial effect is described in detail in above-described specific embodiment, Ying Li
Solution is not intended to restrict the invention the foregoing is merely presently most preferred embodiment of the invention, all in principle model of the invention
Interior done any modification, supplementary, and equivalent replacement etc. are enclosed, should all be included in the protection scope of the present invention.
Claims (7)
1. a kind of Tai Ji tutor auxiliary platform equipment based on breathing detection, which is characterized in that including sound transducer, respiratory monitor
And the communication unit communicated for realizing sound transducer with respiratory monitor, in which:
Sound transducer is used to acquire breathing acoustical signal when people's movement, and converts analog voltage letter for the breathing acoustical signal
Number, which is transmitted to respiratory monitor by communication unit；
Respiratory monitor includes signal processing module and signal display module, wherein signal processing module believes analog voltage
Number carry out noise reduction process, i.e., according to setting classification thresholds classify to analog voltage signal, be divided into expiration type and air-breathing class
Type；Respiratory rate and breathing accounting and exercise heart rate are calculated according to classification results simultaneously；Signal display module is used for breathing
Frequency and breathing accounting and exercise heart rate are shown.
2. the Tai Ji tutor auxiliary platform equipment based on breathing detection as described in claim 1, which is characterized in that in signal processing mould
In block, the process of noise reduction process is carried out to analog voltage signal are as follows:
Classification thresholds are set as 400, the analog voltage signal that will be above 400 is defined as corresponding exhalation process, what output was indicated with 1
High level；It is defined as corresponding breathing process less than the analog voltage signal higher than 400, exports the low level indicated with 0, height electricity
Gentle low level shown by waveform diagram, that is, the respiratory waveform figure after forming noise reduction.
3. the Tai Ji tutor auxiliary platform equipment based on breathing detection as described in claim 1, which is characterized in that after obtaining noise reduction
Respiratory waveform figure after, according to respiratory waveform figure, record current time in air-breathing to expiration Turning Point according to time sequencing
t1；It exhales in next time and arrives air-breathing Turning Point, record current time t2；In next air-breathing to expiration Turning Point, record is current
Time t3；Then t3-t1 is 1 breathing time, obtains respiratory rate with this；
T2-t1 is 1 expiratory duration, and t3-t2 is 1 inspiratory duration, obtains tidal air accounting with this；
Exercise heart rate can be obtained according to respiratory rate and the calculating of tidal air accounting.
4. the Tai Ji tutor auxiliary platform equipment based on breathing detection as described in claim 1, which is characterized in that the monitoring of respiration
Device further includes for realizing the breathing classifier classified automatically to analog voltage signal；
The breathing classifier, which is that the neural network to be made of LSTM unit, convolutional layer and full articulamentum is trained, to be obtained；Mould
After quasi- voltage signal converts breath signal matrix according to a certain period of time, after being input to breathing classifier, it is computed, output should
The corresponding type of respiration of analog voltage signal in time cycle.
5. the Tai Ji tutor auxiliary platform equipment based on breathing detection as claimed in claim 4, which is characterized in that breath signal matrix
Size be [2n, 1], wherein 2nIt indicates n times of 2, indicates the specimen sample time cycle, i.e. a time cycle includes 2nIt is a to adopt
At sample time point, 1 indicates the corresponding analog voltage signal value of sampling time point.
6. the Tai Ji tutor auxiliary platform equipment based on breathing detection as claimed in claim 4, which is characterized in that breathing classifier is pressed
Sequence according to data flow successively includes input layer, LSTM unit, the first full articulamentum, the second full articulamentum, continuous 7 matrix rulers
It is very little successively decrease, the convolutional layer that port number is incremental, the full articulamentum of third and output layer.
7. the Tai Ji tutor auxiliary platform equipment based on breathing detection as claimed in claim 4, which is characterized in that breathing classifier is defeated
Breathing classification results out are shown in the form of waveform diagram in signal display module.
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| JP2014064675A (en) * | 2012-09-25 | 2014-04-17 | Yamaguchi Prefectural Industrial Technology Institute | Non-constraint apnea detection system, method therefor and program therefor |
| CN106175772A (en) * | 2016-08-30 | 2016-12-07 | 徐雁 | A kind of sleep apnea monitoring method and system |
| CN108720837A (en) * | 2017-04-18 | 2018-11-02 | 英特尔公司 | Mthods, systems and devices for detecting respiration phase |
| US20190038180A1 (en) * | 2015-06-14 | 2019-02-07 | Facense Ltd. | Virtual coaching based on respiration signals |
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2019
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005066044A (en) * | 2003-08-25 | 2005-03-17 | Konica Minolta Medical & Graphic Inc | Respiratory sound data processor and program |
| CN101128150A (en) * | 2004-07-23 | 2008-02-20 | 以康源有限公司 | Apparatus and method for breathing pattern determination using a non-contact microphone |
| CN101360537A (en) * | 2006-01-20 | 2009-02-04 | 欧姆龙健康医疗事业株式会社 | Respiration training machine for simply judging respiring state and respiration training program product |
| WO2008139380A2 (en) * | 2007-05-14 | 2008-11-20 | Koninklijke Philips Electronics N.V. | System and method for guiding breathing exercises |
| JP2014064675A (en) * | 2012-09-25 | 2014-04-17 | Yamaguchi Prefectural Industrial Technology Institute | Non-constraint apnea detection system, method therefor and program therefor |
| US20190038180A1 (en) * | 2015-06-14 | 2019-02-07 | Facense Ltd. | Virtual coaching based on respiration signals |
| CN106175772A (en) * | 2016-08-30 | 2016-12-07 | 徐雁 | A kind of sleep apnea monitoring method and system |
| CN108720837A (en) * | 2017-04-18 | 2018-11-02 | 英特尔公司 | Mthods, systems and devices for detecting respiration phase |
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Application publication date: 20191022 |