CN103815896A - Mental fatigue monitoring method, device and system and mobile processing terminal - Google Patents
Mental fatigue monitoring method, device and system and mobile processing terminal Download PDFInfo
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- CN103815896A CN103815896A CN201410031167.4A CN201410031167A CN103815896A CN 103815896 A CN103815896 A CN 103815896A CN 201410031167 A CN201410031167 A CN 201410031167A CN 103815896 A CN103815896 A CN 103815896A
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Abstract
The invention discloses a mental fatigue monitoring method, device and system and a mobile processing terminal. The metal fatigue monitoring method is characterized in that personalized calibration is performed on a monitored person to acquire characteristic parameters of the monitored person under the non-fatigue state, and the characteristic parameters are taken as basic parameters. The mental fatigue monitoring method includes: acquiring electrocardiograph signals of the monitored person in real time and generating an HRV (heart rate variability) sequence according to the electrocardiograph signals; subjecting the HRV sequence to time-domain analysis to acquire time-domain characteristic parameter SDNN (standard diviation of NN intervals); subjecting to the HRV sequence to time-and frequency-domain analysis to acquire a frequency-domain characteristic parameter and curves of the frequency domain characteristic parameters changing over time, wherein the frequency domain characteristic parameters include TP, LF and HF; comparing the time-domain characteristic parameter as well as the frequency-domain characteristics parameter with the basic parameters and judging whether the monitored person enters the mental fatigue state or not in real time. By the mental fatigue monitoring method, mental fatigue can be monitored conveniently and rapidly, judgment standards can be set for different monitored persons, and accuracy of mental fatigue monitoring results can be improved.
Description
Technical field
The present invention relates to a kind of mental fatigue monitoring method, device, system and mobile processing terminal.
Background technology
Along with the continuous increase of modern society's operating pressure, life stress, all there is mental fatigue in various degree in most people, can show as that mood agitation, attention lax, bradykinesia etc.If mental fatigue can not be found and be releived in time, can cause very large impact to cardiovascular and function of nervous system, even cause some psychogenic disorders, serious harm health.
Existing mental fatigue detection means is mainly divided into subjective assessment method and objective evaluation method two classes:
Subjective assessment method is generally undertaken by questionnaire form, and detected person determines the mental fatigue degree of self by the mode answering a questionnaire, and this mode is subject to the impact of detected person's subjective factors larger, and testing result is inaccurate.
Objective evaluation method is mainly from medical angle, measure detected person's index of correlation by medical apparatus, determine detected person's mental fatigue degree according to index, specifically can comprise body fluid detection, electroencephalogram identification, electro-oculogram identification, heart rate variability identification, human body limb state recognition etc.Just there is following problem in this mode: first, while judging degree of fatigue, can adopt the universal standard, still different detected persons' health, living habit, history of disease etc. all exist larger difference, cause the testing result accuracy of this mode lower; Secondly, this mode needs complicated checkout equipment, and testing staff's operant skill is had relatively high expectations.
Summary of the invention
The embodiment of the present invention provides a kind of mental fatigue monitoring method, device, system and mobile processing terminal, carries out simply and easily the real-time detection of mental fatigue, and improves the accuracy of testing result.
For this reason, the invention provides following technical scheme:
A kind of mental fatigue monitoring method, carries out personalization to detected person and demarcates, and obtain the characteristic parameter of this detected person under non-fatigue state, and using described characteristic parameter as basic parameter, described method comprises:
Detected person's electrocardiosignal described in Real-time Collection, and generate heart rate variability HRV sequence according to described electrocardiosignal;
Described HRV sequence is carried out to time-domain analysis, obtain time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Described HRV sequence is carried out to Time-Frequency Analysis, obtain frequency domain character parameter and the time dependent curve of described frequency domain character parameter, described frequency domain character parameter comprises: total power value TP, low frequency power value LF, high frequency power value HF;
Utilize described time domain charactreristic parameter and described frequency domain character parameter and described basic parameter to compare, whether detected person enters mental fatigue state described in real-time judge.
Preferably, described according to described electrocardiosignal generation HRV sequence, comprising:
Analyze described electrocardiosignal, therefrom extract RR interval;
Judge interimly between the RR extracting whether have an abnormal RR interval, if existed, abnormal RR interval is proofreaied and correct, form described HRV sequence.
Preferably,
If a RR interval is the twice of adjacent R R interval, judge that this RR interval, as abnormal RR interval, to the correction of this abnormal RR interval, comprising: described abnormal RR interval is split as to 2 RR intervals;
If a RR interval is the half of adjacent R R interval, judge that this RR interval, as abnormal RR interval, to the correction of this abnormal RR interval, comprising: if two described abnormal RR intervals of continued presence the two is merged into a RR interval;
If a RR interval, is greater than the twice of adjacent R R interval, or a RR interval, is less than the half of adjacent R R interval, judge that this RR interval is as abnormal interval, to the correction of this abnormal RR interval, comprise: calculate the average of described abnormal RR interval adjacent R R interval, described abnormal RR interval is adjusted into described average.
Preferably, described described HRV sequence is carried out to time-domain analysis, obtains time domain charactreristic parameter, comprising:
Wherein, N is the total heart beats monitoring in Preset Time section, RR
jbe j RR interval,
for the RR interval meansigma methods of N heartbeat.
Preferably, described described HRV sequence is carried out to Time-Frequency Analysis, obtains frequency domain character parameter and the time dependent curve of described frequency domain character parameter, comprising:
According to default sample frequency, described HRV sequence is carried out to resampling, form uniform sampling signal;
Determine a time window function according to temporal resolution and frequency resolution, adopt short time discrete Fourier transform to carry out Time-Frequency Analysis to described uniform sampling signal, obtain described frequency domain character parameter and described curve.
Preferably, describedly determine a time window function according to temporal resolution and frequency resolution, comprising:
Adjust the window size of described time window function, described temporal resolution △ t and described frequency resolution △ f met:
Preferably, the described described time domain charactreristic parameter and described frequency domain character parameter and described basic parameter of utilizing compares, and whether detected person enters mental fatigue state described in real-time judge, comprising:
If compared with described basic parameter, described time domain charactreristic parameter SDNN rises, and described frequency domain character parameter TP rises, LF rises, HF declines, and judges that described detected person enters mental fatigue state.
Preferably, described frequency domain character parameter also comprises LF/HF ratio, and described method also comprises:
If described detected person enters mental fatigue state, determine this detected person's mental fatigue grade according to described LF/HF ratio.
Preferably, described frequency domain character parameter also comprises LF/HF ratio, and described method also comprises:
Draw detected person's mental fatigue curve according to described LF/HF ratio, and according to described mental fatigue curve calculation fatigue curve rate of change.
A kind of mental fatigue monitoring device, described device comprises: ecg signal acquiring module, master controller and wireless transport module, described master controller communicates with described ecg signal acquiring module, described wireless transport module respectively;
Described ecg signal acquiring module, for Real-time Collection detected person's electrocardiosignal, and is sent to described master controller;
Described master controller, for generate heart rate variability HRV sequence according to described electrocardiosignal, and controls described wireless transport module described HRV sequence is sent to the mobile processing terminal of described mental fatigue monitoring device outside.
Preferably, described monitoring device also comprises loudspeaker arrangement,
Described loudspeaker arrangement is connected with described master controller, the warning message sending for playing described master controller, and described warning message is sent to described master controller by described mobile processing terminal by described wireless transport module.
A kind of mobile processing terminal, described terminal comprises:
Receiving element, the heart rate variability HRV sequence sending for receiving the mental fatigue monitoring device of described mobile processing terminal outside;
Time-domain analysis unit, for described HRV sequence is carried out to time-domain analysis, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Frequency-domain analysis unit, for described HRV sequence is carried out to Time-Frequency Analysis, obtains frequency domain character parameter and the time dependent curve of described frequency domain character parameter, and described frequency domain character parameter comprises: total power value TP, low frequency power value LF, high frequency power value HF;
Judging unit, for utilizing described time domain charactreristic parameter and described frequency domain character parameter and basic parameter to compare, whether real-time judge detected person enters mental fatigue state, and described basic parameter is the characteristic parameter of described detected person under non-fatigue state.
Preferably, described frequency domain character parameter also comprises LF/HF ratio, and described terminal also comprises:
Tired classification unit, in the time that described detected person enters mental fatigue state, determines this detected person's mental fatigue grade according to described LF/HF ratio.
Preferably, described frequency domain character parameter also comprises LF/HF ratio, and described terminal also comprises:
Computing unit, for draw detected person's mental fatigue curve according to described LF/HF ratio, and according to described mental fatigue curve calculation fatigue curve rate of change.
A kind of mental fatigue monitoring system, described system comprises: above-mentioned mental fatigue monitoring device and above-mentioned mobile processing terminal.
Mental fatigue monitoring method of the present invention, device, system and mobile processing terminal disclose following technique effect:
Adopt technical solution of the present invention, first wear mental fatigue monitoring device of the present invention detected person during in non-fatigue state it is carried out to personalization demarcation, acquisition can be served as the basic parameter of mental fatigue criterion; Then wear monitoring device by detected person when needed, Real-time Collection detected person's electrocardiosignal, and convert HRV sequence to and be sent to mobile processing terminal, from HRV sequence, extracted time domain and frequency domain character parameter by mobile processing terminal, and compare with basic parameter, carry out mental fatigue judgement.So scheme just can realize the fast and easy detection of mental fatigue, for different detected persons, different criterions is set simultaneously, also can improve the accuracy of mental fatigue testing result of the present invention.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, the accompanying drawing the following describes is only some embodiment that record in the application, for those of ordinary skills, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the formation schematic diagram of mental fatigue monitoring system of the present invention;
Fig. 2 be in the present invention mental fatigue monitoring device wear schematic diagram;
Fig. 3 is the flow chart of mental fatigue monitoring method embodiment 1 of the present invention;
Fig. 4 is the electrocardiosignal schematic diagram in a cardiac cycle in the present invention;
Fig. 5 is the formation schematic diagram of a kind of implementation of ecg signal acquiring module in the present invention;
Fig. 6 is the flow chart of mental fatigue monitoring method embodiment 2 of the present invention;
Fig. 7 is the flow chart of mental fatigue monitoring method embodiment 3 of the present invention;
Fig. 8 is the schematic diagram of mental fatigue curve in the present invention.
The specific embodiment
In order to make those skilled in the art person understand better the present invention program, below in conjunction with drawings and embodiments, the embodiment of the present invention is described in further detail.
Referring to Fig. 1, show the formation schematic diagram of mental fatigue monitoring system of the present invention, can comprise mental fatigue monitoring device 10 and mobile processing terminal 20, between the two, can communicate by wired or wireless mode.
Wherein, monitoring device simple in structure, area occupied is little, can conveniently be worn on it detected person, wear a kind of implementation of monitoring device as the present invention, can be shown in Figure 2, electrocardioelectrode sheet is affixed on to detected person front 2 points: the intersection point of right border of sternum the 4th intercostal point, left midaxillary line and the 5th intercostal, two electrodes of checkout gear are fixed on electrode slice by the mode of buckle, Real-Time Monitoring detected person's electrocardiosignal.Certainly, monitoring device also can be worn on other position of detected person's health, and as positions such as both hands, bilateral radial arterys, the present invention can be not specifically limited this, as long as can conveniently monitor user's electrocardiosignal.
Particularly, monitoring device in the present invention can comprise ecg signal acquiring module 11, master controller 12 and wireless transport module 13, master controller communicates with ecg signal acquiring module, wireless transport module respectively, and above-mentioned each module all will be accepted the running voltage that energy supply control module 15 provides.Wherein, ecg signal acquiring module, for Real-time Collection detected person's electrocardiosignal, and is sent to master controller; Master controller, for generating HRV sequence according to electrocardiosignal, and controls wireless transport module described HRV sequence is sent to mobile processing terminal, carries out the processing such as mental fatigue detection, tired grade judgement by mobile processing terminal.
It should be noted that, ecg signal acquiring module can be embodied as two electrodes, changes by the electric potential difference of measuring health different parts, obtains electrocardiosignal.The master controller of monitoring device, wireless transport module, loudspeaker arrangement (are mainly used in receiving the warning message that mobile processing terminal sends, carry out fatigue warning to detected person, wouldn't describe in detail herein), an electrode package of energy supply control module, ecg signal acquiring module together, specifically can schematic diagram shown in Figure 2 in the circle part in the upper left corner; Another electrode of ecg signal acquiring module encapsulates alone, specifically can schematic diagram shown in Figure 2 in the circle part in the lower right corner.
In addition, it should be noted that, monitoring device can communicate by wireless transport module and mobile processing terminal, also can communicate by wired mode, now just require detected person to carry mobile processing terminal, and in order not make troubles to detected person, the data connecting line between monitoring device and mobile processing terminal is unsuitable long.Certainly, as a kind of optimal case, or control makes to communicate with wireless mode between monitoring device and mobile blood processor.
Mobile processing terminal can be the special equipment with function of the present invention, also can be presented as the intelligent terminal's (as mobile phone, PDA, computer, flat board etc.) who has loaded function of the present invention, and the present invention can not limit this.The concrete function introduction that can vide infra wouldn't be described in detail herein.
Below in conjunction with mental fatigue monitoring method of the present invention, the function of monitoring device, the each parts of mobile processing terminal is explained.
It should be noted that, utilizing before mental fatigue monitoring system of the present invention carries out degree of fatigue monitoring, can select the opportunity of a detected person in non-fatigue state, first detected person being carried out to personalization demarcates, physical trait (health, living habit, history of disease etc.) for detected person individual is determined basic parameter, judges that whether detected person is in mental fatigue state and concrete tired grade so that follow-up according to this basic parameter.Wherein, basic parameter can comprise time domain charactreristic parameter and the frequency domain character parameter of detected person's electrocardiosignal.
Referring to Fig. 3, show the flow chart of mental fatigue monitoring method embodiment 1 of the present invention, can comprise:
Detected person is carried out to personalization to demarcate, obtain after this detected person's basic parameter, can make detected person wear monitoring device of the present invention, this detected person's of Real-time Collection electrocardiosignal, and then be sent to master controller, electrocardiosignal is converted to heart rate variability HRV(Heart Rate Variability, refer to the fine difference between successively heart beating interval, the single numerical value calculating thus comprise sympathetic and two kinds of impacts of parasympathetic, and it has reflected autonomic nervous system and Respiratory control function).
First, ecg signal acquiring module for example, gathers original electrocardiosignal according to certain sample frequency (more than being generally 500Hz, 512Hz), referring to Fig. 4, shows an electrocardiosignal schematic diagram in cardiac cycle.
Secondly, in order to form totally electrocardiosignal clearly, ecg signal acquiring module also will be nursed one's health original electrocardiographicdigital signal, removes myoelectricity interference, motion artifacts etc., simplifies subsequent processes.
Finally, master controller receive and analyze ecg signal acquiring module send conditioning after electrocardiosignal, therefrom extract RR interval series, obtain HRV sequence.
It should be noted that, correct reliable in order to guarantee that HRV analyzes, in the data of RR interval, can not mix the interval that has non-hole heartbeat, otherwise will make HRV analysis result produce error, the test result that even must make mistake.In addition, in order further to improve the accuracy that HRV analyzes, after extracting RR interval series, also can judge one by one interim between all RR that extract whether have an abnormal RR interval, if existed, need abnormal RR interval to proofread and correct processing; If there is no, can directly utilize RR interval series to form HRV sequence.
In the present invention program, according to the different origins of abnormal RR interval, abnormal RR interval is divided into three kinds, the feature to various abnormal RR intervals and correcting mode explain respectively below.
Abnormal RR interval of the first
Feature is: abnormal RR interval is the twice of normal RR interval, also, if the twice that RR interval is adjacent R R interval judges that this RR interval is as abnormal RR interval.
This abnormal RR interval, normally causes due to undetected R ripple, and corresponding trimming process is: abnormal RR interval is split as to 2 RR intervals, is inserted in HRV sequence.
It should be noted that, adjacent R R interval, can be understood as former and later two RR intervals that are close to RR to be judged interval; Or, in order to improve judgment accuracy, also can be in the front and back of RR to be judged interval more options several RR interval, use as the adjacent R R interval of RR to be judged interval, for example, four the RR intervals of front and back that close on RR to be judged interval.
In addition, it should be noted that, carrying out abnormal RR interval while judging, can directly use the adjacent R R interval of choosing to compare with RR to be judged interval; Also can first calculate after date between the average RR of at least two adjacent R R intervals, utilize average RR interval to compare with RR to be judged interval, the present invention can be not specifically limited this.
Abnormal RR interval of the second
Feature is: abnormal RR interval is the half of normal RR interval, also, if the half that RR interval is adjacent R R interval judges that this RR interval is as abnormal RR interval.
This abnormal RR interval, may wrong have been regarded T ripple as R ripple and cause, and corresponding trimming process is: if two this abnormal RR intervals of continued presence are merged into the two a RR interval, be inserted in HRV sequence.
It should be noted that, if only there is an above-mentioned abnormal RR interval, cannot utilize merging mode timing, can reject according to actual needs this abnormal RR interval, form an ignore in this position.Because HRV sequence itself is not just uniform sampling, and follow-up in the time extracting frequency domain character parameter, also need HRV sequence to carry out resampling, therefore minority ignore can't exert an influence to judged result of the present invention.
In addition, can, with reference to above introducing, repeat no more for the explanation of adjacent R R interval and concrete judge process herein.
The third abnormal RR interval
Feature is: RR interval is excessive or too small, as, RR interval to be judged, is greater than the twice of normal RR interval, can be considered that RR interval is excessive, is judged as abnormal RR interval; Or RR interval to be judged, is less than the half of normal RR interval, can be considered that RR interval is too small, be judged as abnormal RR interval.
This abnormal RR interval may be that the reasons such as the abnormal operation in gatherer process cause, corresponding trimming process is: the average of calculating the RR interval adjacent with this abnormal RR interval, abnormal RR interval, is adjusted into described average, and the RR interval after adjusting is inserted in HRV sequence.
Explanation for adjacent R R interval and concrete judge process can, with reference to above introducing, also repeat no more herein.
It should be noted that, if RR interval to be judged approaches the twice of normal RR interval, can judge that this RR interval to be judged belongs to abnormal RR interval of the first; If a RR interval to be judged, approaches the half of normal RR interval, also this RR to be judged interval, can be judged to be to the abnormal RR of the second interval, that is to say, be greater than the twice of normal RR interval or cross the half that is less than normal RR interval so long as not crossing, all do not belong to the third abnormal RR interval.
In addition, it should be noted that, ecg signal acquiring module can adopt integrated chip, also can realize by discrete component.As one signal, ecg signal acquiring module can be presented as the BMD101 chip of Shen Nian company, also can be presented as structure shown in Fig. 5, and the present invention can be not specifically limited this.
Master controller utilizes the electrocardiosignal of ecg signal acquiring module Real-Time Monitoring to generate HRV sequence, and be sent to the mobile processing terminal of monitoring device outside by wireless transport module, by mobile processing terminal analysis of HRV sequence, obtain corresponding time domain charactreristic parameter and frequency domain character parameter, and then in conjunction with this two aspect, detected person is carried out to fatigue detecting.
Time domain charactreristic parameter mainly refers to: all standard deviation SDNN(Standard Diviation of NN intervals of normal sinus heartbeat RR interval), the computing formula of SDNN can be presented as:
Wherein, N is the total heart beats monitoring in Preset Time section, and Preset Time section is take 2 hours as example, and N refers to the total heart beats that monitoring device monitored in 2 hours; RR
jit is j RR interval;
for the RR interval meansigma methods of N heartbeat; N-1 is for guaranteeing the unbiasedness of this standard deviation.
Frequency domain character parameter mainly refers to: total power value TP, low frequency power value LF(are greatly about between 0.04～0.15Hz), high frequency power value HF(is greatly between 0.15～0.4Hz).
Because HRV sequence is nonuniform sampling, cannot directly carry out frequency domain transform, therefore, before extracting frequency domain character parameter, first carry out pretreatment (being mainly resampling) to HRV sequence, make it form uniform sampling signal.As the pretreated a kind of implementation of the present invention, can adopt after Cubic Spline Method interpolation HRV sequence, carry out resampling with the sample frequency of 5Hz.
After resampling forms uniform sampled signal, can carry out the conversion process of time domain to frequency domain, particularly, the present invention can adopt short time discrete Fourier transform (STFT, Short-Time Fourier Transform) to realize this process.The basic thought of short time discrete Fourier transform be with a time width enough narrow fixing window function take advantage of time signal, the signal taking out can be seen as stably, then this segment signal taking out is carried out to Fourier transform, just can reflect the spectral change rule in this time width, if allow this fixing window function move along time shaft, that just can obtain the time dependent rule of signal spectrum (being the time dependent curve of frequency domain character parameter), and then therefrom extracts above-mentioned frequency domain character parameter.
The formula of short time discrete Fourier transform can be presented as:
In formula, ω (n) is time window function, and the window of time window function is less, and temporal resolution is higher, and frequency resolution is lower; Otherwise, if the window of time window function is larger, temporal resolution is just lower, frequency resolution is higher, in order to reach best Time-Frequency Analysis effect, should make temporal resolution △ t and frequency resolution △ f meet: △ t* △ f >=1/4 π, the width (being window size) of window should adapt with the local stationary length of signal simultaneously.
From above introducing, before detected person is carried out to fatigue detecting, first carry out personalization to this detected person and demarcate, obtain its basic parameter under non-fatigue state, and be stored in mobile processing terminal, as judging this detected person's fatigue whether examination criteria.
It should be noted that, a mobile processing terminal can be only for a detected person, and now, mobile processing terminal only need to be preserved this detected person's basic parameter; Or, a mobile processing terminal also can be for different detected persons, now mobile processing terminal is except will preserving every detected person's basic parameter, the user profile that also will preserve basic parameter and detected person is (as user name, the essential informations such as user's sex, height, body weight, age) between corresponding relation, so just can realize different detected person's correspondences object of different basic parameters separately, improve the accuracy of fatigue detection result of the present invention.
After obtaining SDNN, TP, LF, HF by step 102,103, can call this detected person's of mobile processing terminal preservation basic parameter, if compared with basic parameter, HRV time domain measurement index S DNN rises, total power value TP in HRV frequency domain measurement index rises, low-frequency range performance number LF rises, and high band performance number HF declines, and judges that detected person enters mental fatigue state; If various features parameter, compared with basic parameter, without significant change, judges that detected person is in non-mental fatigue state, so just realize simply and easily the real-time detection of mental fatigue.
Referring to Fig. 6, show the flow chart of mental fatigue monitoring method embodiment 2 of the present invention, can comprise:
Step 201～204 are identical with step 101～104, repeat no more herein.It should be noted that, the present embodiment frequency domain characteristic parameter also comprises LF/HF ratio, corresponding, judge that the mode whether detected person enters mental fatigue state is: if SDNN rises, TP rises, LF rises, HF declines and LF/HF ratio rises, judge that detected person enters mental fatigue state; If various features parameter, compared with basic parameter, without significant change, judges that detected person is in non-mental fatigue state.
The present invention, except carrying out fatigue detecting detected person, also can, in the time that definite detected person enters mental fatigue state, further determine its tired grade, to give a warning to detected person accordingly, points out it suitably to have a rest, and alleviates mental fatigue.
Particularly, can be according to the ratio calculation fatigue exponent between the LF/HF in the LF/HF and the basic parameter that obtain in step 203, the normal span of fatigue exponent can be presented as 1～10, corresponding fatigue state is divided into 3 grades, wherein, the corresponding slight tired grade of fatigue exponent 1～4, the tired grade of 5～7 corresponding moderate, 8～10 corresponding overtired grades.In addition, fatigue exponent is exceeded to 10 situation and be defined as the tired grade of severe.
Like this, just can be definite detected person after mental fatigue state, further clear and definite tired grade, and carry out tired alarm according to presetting of detected person, as detected person is set in it when the overtired grade, sends to it warning of having a rest.Or detected person is set in it in the time that the tired grade of moderate exceedes 3 hours, sends to it warning of having a rest.The condition giving a warning can be set according to s own situation by user, also can set by Default Value, and the present invention can be not specifically limited this.
In addition, it should be noted that, return to after normal condition detected person, also can send the mental status to it and recover, suggestion maintenance waits prompting.
The prompting that above-mentioned tired alarm, spirit have been recovered etc. all can be considered warning message of the present invention, warning message is after mobile processing terminal judges, send in wired or wireless mode the monitoring device that detected person wears and (preferably send to the wireless transport module of monitoring device by wireless mode, and then transfer to master controller by wireless transport module, and sent by main controller controls loudspeaker arrangement), sent to detected person by monitoring device.
Referring to Fig. 7, show the flow chart of mental fatigue monitoring method embodiment 3 of the present invention, can comprise:
Step 301～305 are identical with step 201～205, repeat no more herein.
Mental fatigue curve is for reflecting the degree of fatigue (specifically can by fatigue exponent or tired grade represent) of detected person at certain time point, as a kind of example, and schematic diagram that can fatigue curve shown in Figure 8, transverse axis is the time, the longitudinal axis is fatigue exponent.
Fatigue curve can reflect the tired variation tendency of detected person in certain hour section, draw out after fatigue curve, can calculate the tired rate of change obtaining between adjacent two test points, schematic diagram shown in Figure 8, ∠ α is the inclination angle that fatigue exponent rises, ∠ β is the inclination angle of fatigue recovery, and the larger explanation rate of change of angle is faster, and parasympathetic activity changes faster.
In order to match with the monitoring device of mental fatigue shown in Fig. 1, realize fatigue detecting process of the present invention, mobile processing terminal should comprise with lower unit:
Receiving element, the heart rate variability HRV sequence sending for receiving the mental fatigue monitoring device of described mobile processing terminal outside;
Time-domain analysis unit, for described HRV sequence is carried out to time-domain analysis, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Frequency-domain analysis unit, for described HRV sequence is carried out to Time-Frequency Analysis, obtains frequency domain character parameter and the time dependent curve of described frequency domain character parameter, and described frequency domain character parameter comprises: total power value TP, low frequency power value LF, high frequency power value HF;
Judging unit, for utilizing described time domain charactreristic parameter and described frequency domain character parameter and basic parameter to compare, whether real-time judge detected person enters mental fatigue state, and described basic parameter is the characteristic parameter of described detected person under non-fatigue state.
Detailed process can be introduced referring to embodiment of the method above, repeats no more herein.
In addition, frequency domain character parameter also can comprise LF/HF ratio, like this, is judging that detected person enters after mental fatigue state, and the present invention also can further determine the current residing tired grade of detected person, moves processing terminal also can comprise corresponding to this:
Tired classification unit, in the time that described detected person enters mental fatigue state, determines this detected person's mental fatigue grade according to described LF/HF ratio.
Or frequency domain character parameter also can comprise LF/HF ratio, like this, in order to obtain the tired variation tendency of detected person in certain hour section, mobile processing terminal also can comprise:
Computing unit, for draw detected person's mental fatigue curve according to described LF/HF ratio, and according to described mental fatigue curve calculation fatigue curve rate of change.
The present invention program can describe in the general context of the computer executable instructions of being carried out by computer, for example program unit.Usually, program unit comprises and carries out particular task or realize routine, program, object, assembly, data structure of particular abstract data type etc.Also can in distributed computing environment, put into practice the present invention program, in these distributed computing environment, be executed the task by the teleprocessing equipment being connected by communication network.In distributed computing environment, program unit can be arranged in the local and remote computer-readable storage medium including memory device.
Each embodiment in this description all adopts the mode of going forward one by one to describe, between each embodiment identical similar part mutually referring to, what each embodiment stressed is and the difference of other embodiment.Especially,, for system embodiment, because it is substantially similar in appearance to embodiment of the method, so describe fairly simplely, relevant part is referring to the part explanation of embodiment of the method.System embodiment described above is only schematic, the wherein said unit as separating component explanation can or can not be also physically to separate, the parts that show as unit can be or can not be also physical locations, can be positioned at a place, or also can be distributed on multiple NEs.Can select according to the actual needs some or all of module wherein to realize the object of the present embodiment scheme.Those of ordinary skills, in the situation that not paying creative work, are appreciated that and implement.
Above the embodiment of the present invention is described in detail, has applied the specific embodiment herein the present invention is set forth, the explanation of above embodiment is just for helping to understand method and apparatus of the present invention; , for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention meanwhile.
Claims (15)
1. a mental fatigue monitoring method, is characterized in that, detected person is carried out to personalization and demarcate, and obtain the characteristic parameter of this detected person under non-fatigue state, and using described characteristic parameter as basic parameter, described method comprises:
Detected person's electrocardiosignal described in Real-time Collection, and generate heart rate variability HRV sequence according to described electrocardiosignal;
Described HRV sequence is carried out to time-domain analysis, obtain time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Described HRV sequence is carried out to Time-Frequency Analysis, obtain frequency domain character parameter and the time dependent curve of described frequency domain character parameter, described frequency domain character parameter comprises: total power value TP, low frequency power value LF, high frequency power value HF;
Utilize described time domain charactreristic parameter and described frequency domain character parameter and described basic parameter to compare, whether detected person enters mental fatigue state described in real-time judge.
2. method according to claim 1, is characterized in that, described according to described electrocardiosignal generation HRV sequence, comprising:
Analyze described electrocardiosignal, therefrom extract RR interval;
Judge interimly between the RR extracting whether have an abnormal RR interval, if existed, abnormal RR interval is proofreaied and correct, form described HRV sequence.
3. method according to claim 2, is characterized in that,
If a RR interval is the twice of adjacent R R interval, judge that this RR interval, as abnormal RR interval, to the correction of this abnormal RR interval, comprising: described abnormal RR interval is split as to 2 RR intervals;
If a RR interval is the half of adjacent R R interval, judge that this RR interval, as abnormal RR interval, to the correction of this abnormal RR interval, comprising: if two described abnormal RR intervals of continued presence the two is merged into a RR interval;
If a RR interval, is greater than the twice of adjacent R R interval, or a RR interval, is less than the half of adjacent R R interval, judge that this RR interval is as abnormal interval, to the correction of this abnormal RR interval, comprise: calculate the average of described abnormal RR interval adjacent R R interval, described abnormal RR interval is adjusted into described average.
4. method according to claim 1, is characterized in that, described described HRV sequence is carried out to time-domain analysis, obtains time domain charactreristic parameter, comprising:
Wherein, N is the total heart beats monitoring in Preset Time section, RR
jbe j RR interval,
for the RR interval meansigma methods of N heartbeat.
5. method according to claim 1, is characterized in that, described described HRV sequence is carried out to Time-Frequency Analysis, obtains frequency domain character parameter and the time dependent curve of described frequency domain character parameter, comprising:
According to default sample frequency, described HRV sequence is carried out to resampling, form uniform sampling signal;
Determine a time window function according to temporal resolution and frequency resolution, adopt short time discrete Fourier transform to carry out Time-Frequency Analysis to described uniform sampling signal, obtain described frequency domain character parameter and described curve.
6. method according to claim 5, is characterized in that, describedly determines a time window function according to temporal resolution and frequency resolution, comprising:
Adjust the window size of described time window function, described temporal resolution △ t and described frequency resolution △ f met:
7. method according to claim 1, is characterized in that, the described described time domain charactreristic parameter and described frequency domain character parameter and described basic parameter of utilizing compares, and whether detected person enters mental fatigue state described in real-time judge, comprising:
If compared with described basic parameter, described time domain charactreristic parameter SDNN rises, and described frequency domain character parameter TP rises, LF rises, HF declines, and judges that described detected person enters mental fatigue state.
8. according to the method described in claim 1～7 any one, it is characterized in that, described frequency domain character parameter also comprises LF/HF ratio, and described method also comprises:
If described detected person enters mental fatigue state, determine this detected person's mental fatigue grade according to described LF/HF ratio.
9. according to the method described in claim 1～7, it is characterized in that, described frequency domain character parameter also comprises LF/HF ratio, and described method also comprises:
Draw detected person's mental fatigue curve according to described LF/HF ratio, and according to described mental fatigue curve calculation fatigue curve rate of change.
10. a mental fatigue monitoring device, is characterized in that, described device comprises: ecg signal acquiring module, master controller and wireless transport module, and described master controller communicates with described ecg signal acquiring module, described wireless transport module respectively;
Described ecg signal acquiring module, for Real-time Collection detected person's electrocardiosignal, and is sent to described master controller;
Described master controller, for generate heart rate variability HRV sequence according to described electrocardiosignal, and controls described wireless transport module described HRV sequence is sent to the mobile processing terminal of described mental fatigue monitoring device outside.
11. devices according to claim 10, is characterized in that, described monitoring device also comprises loudspeaker arrangement,
Described loudspeaker arrangement is connected with described master controller, the warning message sending for playing described master controller, and described warning message is sent to described master controller by described mobile processing terminal by described wireless transport module.
12. 1 kinds of mobile processing terminals, is characterized in that, described terminal comprises:
Receiving element, the heart rate variability HRV sequence sending for receiving the mental fatigue monitoring device of described mobile processing terminal outside;
Time-domain analysis unit, for described HRV sequence is carried out to time-domain analysis, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Frequency-domain analysis unit, for described HRV sequence is carried out to Time-Frequency Analysis, obtains frequency domain character parameter and the time dependent curve of described frequency domain character parameter, and described frequency domain character parameter comprises: total power value TP, low frequency power value LF, high frequency power value HF;
Judging unit, for utilizing described time domain charactreristic parameter and described frequency domain character parameter and basic parameter to compare, whether real-time judge detected person enters mental fatigue state, and described basic parameter is the characteristic parameter of described detected person under non-fatigue state.
13. terminals according to claim 12, is characterized in that, described frequency domain character parameter also comprises LF/HF ratio, and described terminal also comprises:
Tired classification unit, in the time that described detected person enters mental fatigue state, determines this detected person's mental fatigue grade according to described LF/HF ratio.
14. according to the terminal described in claim 12 or 13, it is characterized in that, described frequency domain character parameter also comprises LF/HF ratio, and described terminal also comprises:
Computing unit, for draw detected person's mental fatigue curve according to described LF/HF ratio, and according to described mental fatigue curve calculation fatigue curve rate of change.
15. 1 kinds of mental fatigue monitoring systems, is characterized in that, described system comprises: the mobile processing terminal as described in mental fatigue monitoring device and claim 12～14 any one as described in claim 10 or 11.
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