CN103815896B - A kind of mental fatigue monitoring method, device, system and mobile processing terminal - Google Patents
A kind of mental fatigue monitoring method, device, system and mobile processing terminal Download PDFInfo
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- CN103815896B CN103815896B CN201410031167.4A CN201410031167A CN103815896B CN 103815896 B CN103815896 B CN 103815896B CN 201410031167 A CN201410031167 A CN 201410031167A CN 103815896 B CN103815896 B CN 103815896B
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Abstract
The invention discloses a kind of mental fatigue monitoring method, device, system and mobile processing terminal, carry out personalization to detected person to demarcate, obtain the characteristic parameter of this detected person under non-fatigue state, and using described characteristic parameter as basic parameter, described method comprises: the electrocardiosignal of Real-time Collection detected person, and generates HRV sequence according to electrocardiosignal; Time-domain analysis is carried out to HRV sequence, obtains time domain charactreristic parameter SDNN; Carry out Time-Frequency Analysis to HRV sequence, obtain frequency domain character parameter and the time dependent curve of frequency domain character parameter, frequency domain character parameter comprises: TP, LF, HF; Utilize time domain charactreristic parameter and frequency domain character parameter and basic parameter to compare, whether real-time judge detected person enters mental fatigue state.Scheme like this, the fast and easy that just can realize mental fatigue detects, and arranges different criterions simultaneously, also can improve the accuracy of mental fatigue testing result of the present invention for different detected person.
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 mood agitation, attention laxes, bradykinesia etc.If mental fatigue can not be releived by Timeliness coverage, very large impact can be caused on 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 class:
Subjective assessment method is generally undertaken by questionnaire form, and detected person determines the mental fatigue degree of self by the mode answered a questionnaire, and this mode is comparatively large by the impact of detected person's subjective factors, and testing result is inaccurate.
Objective evaluation method is mainly from medical angle, the index of correlation of detected person is measured by medical apparatus, determine the mental fatigue degree of detected person 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, can adopt the universal standard, but the health of different detected person, living habit, history of disease etc. all exists larger difference, cause the testing result accuracy of this mode lower when judging degree of fatigue; Secondly, this mode needs complicated checkout equipment, and requires higher to the operant skill of testing staff.
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 the real-time detection of mental fatigue simply and easily, and improves the accuracy of testing result.
For this reason, the invention provides following technical scheme:
A kind of mental fatigue monitoring method, personalization is carried out to detected person and demarcates, obtain the characteristic parameter of this detected person under non-fatigue state, and using described characteristic parameter as basic parameter, described method comprises:
The electrocardiosignal of detected person described in Real-time Collection, and generate heart rate variability HRV sequence according to described electrocardiosignal;
Time-domain analysis is carried out to described HRV sequence, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Carry out Time-Frequency Analysis to described HRV sequence, 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 described in real-time judge enters mental fatigue state.
Preferably, described according to described electrocardiosignal generation HRV sequence, comprising:
Analyze described electrocardiosignal, therefrom extract RR interval;
Judge interimly whether there is abnormal RR interval between the RR that extracts, if existed, then abnormal RR interval is corrected, form described HRV sequence.
Preferably,
If a RR interval is the twice of adjacent R R interval, then judge that this RR interval is as abnormal RR interval, to the correction of this abnormal RR interval, comprising: described abnormal RR interval is split as 2 RR intervals;
If a RR interval is the half of adjacent R R interval, then judge that this RR interval is as abnormal RR interval, to the correction of this abnormal RR interval, comprising: if continued presence two described abnormal RR interval, then 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, then judge that this RR interval is as outlying interval, to the correction of this abnormal RR interval, comprise: the average calculating described abnormal RR interval adjacent R R interval, is adjusted to described average by described abnormal RR interval.
Preferably, described time-domain analysis is carried out to described HRV sequence, obtains time domain charactreristic parameter, comprising:
Wherein, N is the total heart beats monitored in preset time period, RR
jfor a jth RR interval,
for the RR interval meansigma methods of N number of heartbeat.
Preferably, described Time-Frequency Analysis is carried out to described HRV sequence, obtains frequency domain character parameter and the time dependent curve of described frequency domain character parameter, comprising:
According to default sample frequency, resampling is carried out to described HRV sequence, 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 described in real-time judge enters mental fatigue state, 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, then judge that described detected person enters mental fatigue state.
Preferably, described frequency domain character parameter also comprises LF/HF ratio, then described method also comprises:
If described detected person enters mental fatigue state, then determine the mental fatigue grade of this detected person according to described LF/HF ratio.
Preferably, described frequency domain character parameter also comprises LF/HF ratio, then described method also comprises:
Draw the mental fatigue curve of detected person according to described LF/HF ratio, and calculate fatigue curve rate of change according to described mental fatigue curve.
A kind of mental fatigue monitoring device, 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 the electrocardiosignal of Real-time Collection detected person, and is sent to described master controller;
Described master controller, for generating heart rate variability HRV sequence according to described electrocardiosignal, and controls the mobile processing terminal that described HRV sequence is sent to described mental fatigue monitoring device outside by described wireless transport module.
Preferably, described monitoring device also comprises loudspeaker arrangement,
Described loudspeaker arrangement is connected with described master controller, and for playing the warning message that described master controller sends, 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 that the mental fatigue monitoring device for receiving described mobile processing terminal outside sends;
Time-domain analysis unit, for carrying out time-domain analysis to described HRV sequence, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Frequency-domain analysis unit, for carrying out Time-Frequency Analysis to described HRV sequence, 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;
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, then described terminal also comprises:
Tired level de-termination unit, for when described detected person enters mental fatigue state, determines the mental fatigue grade of this detected person according to described LF/HF ratio.
Preferably, described frequency domain character parameter also comprises LF/HF ratio, then described terminal also comprises:
Computing unit, for drawing the mental fatigue curve of detected person according to described LF/HF ratio, and calculates fatigue curve rate of change according to described mental fatigue curve.
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 when detected person is in non-fatigue state and carry out personalization demarcation to it, obtaining can as the basic parameter of mental fatigue criterion; Then monitoring device is worn by detected person when needed, the electrocardiosignal of Real-time Collection detected person, and convert HRV sequence to and be sent to mobile processing terminal, from HRV sequence, time domain and frequency domain character parameter is extracted by mobile processing terminal, and compare with basic parameter, carry out mental fatigue judgement.The fast and easy that scheme like this just can realize mental fatigue detects, and arranges different criterions simultaneously, also can improve the accuracy of mental fatigue testing result of the present invention for different detected person.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, the accompanying drawing that the following describes is only some embodiments recorded in the application, for those of ordinary skill in the art, other accompanying drawing can also be obtained according to these accompanying drawings.
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 the present invention in a cardiac cycle;
Fig. 5 is the formation schematic diagram of a kind of implementation of center telecommunications acquisition module of 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.
Detailed description of the invention
In order to make those skilled in the art person understand the present invention program better, below in conjunction with drawings and embodiments, the embodiment of the present invention is described in further detail.
See Fig. 1, show the formation schematic diagram of mental fatigue monitoring system of the present invention, mental fatigue monitoring device 10 and mobile processing terminal 20 can be comprised, communicate by wired or wireless mode therebetween.
Wherein, the structure of monitoring device is simple, area occupied is little, can conveniently be worn on it detected person, a kind of implementation of monitoring device is worn as the present invention, can be shown in Figure 2, electrocardio electrode plate is affixed on detected person front two point: 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, the electrocardiosignal of Real-Time Monitoring detected person.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 conveniently can monitor the electrocardiosignal of user.
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 accept the running voltage that energy supply control module 15 provides.Wherein, ecg signal acquiring module, for the electrocardiosignal of Real-time Collection detected person, 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, carry out the process 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, by measuring the electric potential difference change of health different parts, obtains electrocardiosignal.The master controller of monitoring device, wireless transport module, loudspeaker arrangement (are mainly used in the warning message receiving the transmission of mobile processing terminal, fatigue warning is carried out to detected person, wouldn't describe in detail herein), an electrode package of energy supply control module, ecg signal acquiring module together, specifically can the encircled portion in the upper left corner in schematic diagram shown in Figure 2; Another electrode of ecg signal acquiring module encapsulates alone, specifically can the encircled portion in the lower right corner in schematic diagram shown in Figure 2.
In addition, it should be noted that, monitoring device communicates by wireless transport module and mobile processing terminal, also can communicate in a wired fashion, now just require that detected person carries with mobile processing terminal, and in order to 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 to make wirelessly to communicate between monitoring device and mobile blood processor.
Mobile processing terminal can be the special equipment with function of the present invention, and also can be presented as the intelligent terminal's (as mobile phone, PDA, computer, flat board etc.) being loaded with function of the present invention, the present invention can not limit this.Concrete function can vide infra introduction, wouldn't describe in detail herein.
Explain below in conjunction with the function of mental fatigue monitoring method of the present invention to monitoring device, each parts of mobile processing terminal.
It should be noted that, utilizing before mental fatigue monitoring system of the present invention carries out degree of fatigue monitoring, a detected person can be selected to be in the opportunity of non-fatigue state, first carry out personalization to detected person to demarcate, physical trait (health, living habit, history of disease etc.) for detected person individual determines basic parameter, judges 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.
See Fig. 3, show the flow chart of mental fatigue monitoring method embodiment 1 of the present invention, can comprise:
Step 101, the electrocardiosignal of Real-time Collection detected person, and generate heart rate variability HRV sequence according to described electrocardiosignal.
Carry out personalization to detected person to demarcate, after obtaining the basic parameter of this detected person, detected person can be made to wear monitoring device of the present invention, the electrocardiosignal of this detected person of Real-time Collection, and then be sent to master controller, electrocardiosignal is converted to heart rate variability HRV(HeartRateVariability, refer to the fine difference successively between heartbeat interval, namely the single numerical value calculated thus comprise sympathetic and parasympathetic two kinds impact, it reflects autonomic nervous system and Respiratory control function).
First, ecg signal acquiring module such as, gathers original electrocardiosignal according to certain sample frequency (being generally more than 500Hz, 512Hz), see Fig. 4, shows the electrocardiosignal schematic diagram in a cardiac cycle.
Secondly, in order to form clean electrocardiosignal clearly, ecg signal acquiring module also will be nursed one's health original electro-cardiologic signals, removes myoelectricity interference, motion artifacts etc., simplifies subsequent processes.
Finally, master controller receives and electrocardiosignal after analyzing the conditioning that ecg signal acquiring module sends, therefrom extracts RR interval series, obtains HRV sequence.
It should be noted that, reliably correct in order to ensure that HRV analyzes, the interval having non-sinus heartbeat can not be mixed in the data of RR interval, otherwise HRV analysis result will be made to produce error, the test result that even must make mistake.In addition, in order to improve the accuracy that HRV analyzes further, after extracting RR interval series, also can judge interim between all RR of extracting whether to there is abnormal RR interval one by one, if existed, then needing to carry out correction process to abnormal RR interval; If there is no, then RR interval series can be directly utilized 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, respectively the feature of various abnormal RR interval and correcting mode is explained below.
The first abnormal RR interval
Feature is: abnormal RR interval is the twice of normal RR-intervals, also, if RR interval is the twice of adjacent R R interval, then 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 2 RR intervals, is inserted in HRV sequence.
It should be noted that, adjacent R R interval, can be understood as and former and later two RR intervals waiting to judge that RR interval is close to; Or, in order to improve judgment accuracy, also can wait to judge the front and back more options of RR interval several RR interval, such as, as waiting that the adjacent R R interval judging RR interval uses, with four the RR intervals of front and back waiting to judge that RR interval closes on.
In addition, it should be noted that, carry out abnormal RR interval judge time, directly can use the adjacent R R interval chosen and wait to judge compared with RR interval; After date between the average RR that also first can calculate at least two adjacent R R intervals, utilizes average RR interval and waits to judge that compared with RR interval, the present invention can be not specifically limited this.
The second abnormal RR interval
Feature is: abnormal RR interval is the half of normal RR-intervals, also, if RR interval is the half of adjacent R R interval, then judges that this RR interval is as abnormal RR interval.
T ripple wrong may have been regarded R ripple and cause by this abnormal RR interval, and corresponding trimming process is: if continued presence two this abnormal RR interval, then the two is merged into a RR interval, be inserted in HRV sequence.
It should be noted that, if only there is an above-mentioned abnormal RR interval, merging mode timing cannot be utilized, this abnormal RR interval can be rejected according to actual needs, form an ignore in this position.Because HRV sequence is not inherently uniform sampling, and follow-up when extracting frequency domain character parameter, also need to carry out resampling to HRV sequence, therefore minority ignore can't have an impact to judged result of the present invention.
In addition, the explanation for adjacent R R interval and concrete judge process can refer to above introduce, repeat no more herein.
The third abnormal RR interval
Feature is: RR interval is excessive or too small, e.g., waits to judge that RR interval is greater than the twice of normal RR-intervals, namely can be considered that RR interval is excessive, be judged as abnormal RR interval; Or, wait to judge that RR interval is less than the half of normal RR-intervals, namely 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 abnormal operation in gatherer process cause, corresponding trimming process is: the average calculating the RR interval adjacent with this abnormal RR interval, abnormal RR interval, is adjusted to described average, and the RR interval after adjustment is inserted in HRV sequence.
Explanation for adjacent R R interval and concrete judge process can refer to above introduce, also repeat no more herein.
It should be noted that, if one is waited to judge the twice of RR interval close to normal RR-intervals, can judge that this waits to judge that RR interval belongs to the first abnormal RR interval; If wait for one to judge the half of RR interval close to normal RR-intervals, also this can be waited to judge that RR interval is judged to be the second abnormal RR interval, that is, so long as not being excessively greater than the twice of normal RR-intervals or crossing the half being less than normal RR-intervals, the third abnormal RR interval is not all belonged to.
In addition, it should be noted that, ecg signal acquiring module can adopt integrated chip, also can be realized 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.
Step 102, carries out time-domain analysis to described HRV sequence, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval.
Master controller utilizes the electrocardiosignal of ecg signal acquiring module Real-Time Monitoring to generate HRV sequence, and the mobile processing terminal of monitoring device outside is sent to 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 these two aspects, fatigue detecting is carried out to detected person.
Time domain charactreristic parameter mainly refers to: all standard deviation SDNN(StandardDiviationofNNintervals of normal sinus heartbeat RR interval), the computing formula of SDNN can be presented as:
Wherein, N is the total heart beats monitored in preset time period, and preset time period was for 2 hours, and N refers to the total heart beats that monitoring device monitored in 2 hours; RR
jfor a jth RR interval;
for the RR interval meansigma methods of N number of heartbeat; N-1 is for ensureing the unbiasedness of this standard deviation.
Step 103, carries out Time-Frequency Analysis to described HRV sequence, and 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.
Frequency domain character parameter mainly refers to: total power value TP, low frequency power value LF(are greatly between 0.04 ~ 0.15Hz), high frequency power value HF(is greatly between 0.15 ~ 0.4Hz).
Because HRV sequence is nonuniform sampling, directly cannot carry out frequency domain transform, therefore, before extraction frequency domain character parameter, first will carry out pretreatment (mainly resampling) to HRV sequence, make it form uniform sampling signal.As the pretreated a kind of implementation of the present invention, after Cubic Spline Method interpolation can being adopted to 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-TimeFourierTransform) to realize this process.The basic thought of short time discrete Fourier transform takes advantage of time signal with the fixing window function that time width is enough narrow, make the signal of taking-up can be seen as stably, then Fourier transform is carried out to this segment signal taken out, just the spectral change rule in this time width can be reflected, if allow this fixing window function move along time shaft, that just can obtain the time dependent rule of signal spectrum (i.e. 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 lower, frequency resolution is higher, in order to reach best Time-Frequency Analysis effect, temporal resolution △ t and frequency resolution △ f should be made to meet: △ t* △ f >=1/4 π, the width (i.e. window size) of window should adapt with the local stationary length of signal simultaneously.
Step 104, utilize described time domain charactreristic parameter and described frequency domain character parameter and described basic parameter to compare, whether detected person described in real-time judge enters mental fatigue state.
From introducing above, before fatigue detecting is carried out to detected person, first to 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 the examination criteria that whether tired this detected person is.
It should be noted that, a mobile processing terminal can only for a detected person, and now, mobile processing terminal only needs the basic parameter preserving this detected person; Or, a mobile processing terminal also can for different detected person, now move processing terminal except will preserving the basic parameter of every detected person, also to preserve the user profile of basic parameter and detected person (as user name, the essential information such as user's sex, height, body weight, age) between corresponding relation, so just can realize the object of the corresponding respective different basic parameter of different detected person, improve the accuracy of fatigue detection result of the present invention.
After obtaining SDNN, TP, LF, HF by step 102,103, the basic parameter of this detected person that mobile processing terminal is preserved can be called, 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, then judge that detected person enters mental fatigue state; If various features parameter is compared with basic parameter, without significant change, then judge that detected person is in non-mental fatigue state, so just achieves the real-time detection of mental fatigue simply and easily.
See Fig. 6, show the flow chart of mental fatigue monitoring method embodiment 2 of the present invention, can comprise:
Step 201, the electrocardiosignal of Real-time Collection detected person, and generate heart rate variability HRV sequence according to described electrocardiosignal.
Step 202, carries out time-domain analysis to described HRV sequence, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval.
Step 203, Time-Frequency Analysis is carried out to described HRV sequence, 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, LF/HF ratio.
Step 204, utilize described time domain charactreristic parameter and described frequency domain character parameter and described basic parameter to compare, whether detected person described in real-time judge enters mental fatigue state.
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, then judge that detected person enters mental fatigue state; If various features parameter is compared with basic parameter, without significant change, then judge that detected person is in non-mental fatigue state.
Step 205, if described detected person enters mental fatigue state, then determines the mental fatigue grade of this detected person according to described LF/HF ratio.
The present invention, except carrying out except fatigue detecting to detected person, also when determining that detected person enters mental fatigue state, can determine its tired grade further, to give a warning to detected person accordingly, pointing out it suitably to have a rest, and alleviates mental fatigue.
Particularly, can according to the ratio calculation fatigue exponent between the LF/HF in the LF/HF obtained in step 203 and basic parameter, the normal span of fatigue exponent can be presented as 1 ~ 10, corresponding is divided into 3 grades by fatigue state, 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, the situation of fatigue exponent more than 10 is defined as sever fatigue grade.
Like this, just can after determining that detected person is in mental fatigue state, further clear and definite tired grade, and carry out tired alarm according to presetting of detected person, as detected person be set in its be in overtired grade time, namely send to it warning of having a rest.Or detected person is set in it, and to be in the tired grade of moderate constantly little more than 3, sends to it warning of having a rest.The condition given a warning can be set according to own situation by user, and also by Default Value setting, the present invention can be not specifically limited this.
In addition, it should be noted that, after detected person returns to normal condition, also can send the mental status to it and recover, suggestion maintenance waits prompting.
The prompting etc. that above-mentioned tired alarm, spirit have recovered all can be considered warning message of the present invention, warning message is after mobile processing terminal judges, be sent to the monitoring device that detected person wears in a wired or wireless fashion and (send to the wireless transport module of monitoring device preferably 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.
See Fig. 7, show the flow chart of mental fatigue monitoring method embodiment 3 of the present invention, can comprise:
Step 301, the electrocardiosignal of Real-time Collection detected person, and generate heart rate variability HRV sequence according to described electrocardiosignal.
Step 302, carries out time-domain analysis to described HRV sequence, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval.
Step 303, Time-Frequency Analysis is carried out to described HRV sequence, 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, LF/HF ratio.
Step 304, utilize described time domain charactreristic parameter and described frequency domain character parameter and described basic parameter to compare, whether detected person described in real-time judge enters mental fatigue state.
Step 305, if described detected person enters mental fatigue state, then determines the mental fatigue grade of this detected person according to described LF/HF ratio.
Step 301 ~ 305 are identical with step 201 ~ 205, repeat no more herein.
Step 306, draws the mental fatigue curve of detected person according to described LF/HF ratio, and calculates fatigue curve rate of change according to described mental fatigue curve.
Mental fatigue curve 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, can the schematic diagram of fatigue curve shown in Figure 8, transverse axis is the time, and the longitudinal axis is fatigue exponent.
Fatigue curve can reflect the tired variation tendency of detected person in certain hour section, after drawing out fatigue curve, the tired rate of change obtained between adjacent two test points can be calculated, schematic diagram shown in Figure 8, ∠ α is the inclination angle that fatigue exponent rises, ∠ β is the inclination angle of fatigue recovery, and angle larger explanation rate of change is faster, and parasympathetic activity change is 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 that the mental fatigue monitoring device for receiving described mobile processing terminal outside sends;
Time-domain analysis unit, for carrying out time-domain analysis to described HRV sequence, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Frequency-domain analysis unit, for carrying out Time-Frequency Analysis to described HRV sequence, 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;
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 see embodiment of the method above, repeats no more herein.
In addition, frequency domain character parameter also can comprise LF/HF ratio, and like this, after judgement detected person enters mental fatigue state, the present invention also can determine the current residing tired grade of detected person further, moves processing terminal also can comprise corresponding to this:
Tired level de-termination unit, for when described detected person enters mental fatigue state, determines the mental fatigue grade of this detected person 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 drawing the mental fatigue curve of detected person according to described LF/HF ratio, and calculates fatigue curve rate of change according to described mental fatigue curve.
The present invention program can describe in the general context of computer executable instructions, such as program unit.Usually, program unit comprises the routine, program, object, assembly, data structure etc. that perform particular task or realize particular abstract data type.Also can put into practice the present invention program in a distributed computing environment, in these distributed computing environment, be executed the task by the remote processing devices be connected by communication network.In a distributed computing environment, program unit can be arranged in the local and remote computer-readable storage medium comprising 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 see, what each embodiment stressed is the difference with other embodiments.Especially, for system embodiment, because it is substantially similar to embodiment of the method, so describe fairly simple, relevant part illustrates see the part of embodiment of the method.System embodiment described above is only schematic, the wherein said unit illustrated as separating component or can may not be and physically separates, parts as unit display can be or may not be physical location, namely can be positioned at a place, or also can be distributed on multiple NE.Some or all of module wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.Those of ordinary skill in the art, when not paying creative work, are namely appreciated that and implement.
Being described in detail the embodiment of the present invention above, applying detailed description of the invention herein to invention has been elaboration, the explanation of above embodiment just understands method and apparatus of the present invention for helping; Meanwhile, 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.
Claims (14)
1. a mental fatigue monitoring method, is characterized in that, carries out personalization and demarcates, obtain the characteristic parameter of this detected person under non-fatigue state, and using described characteristic parameter as basic parameter, described method comprises to detected person:
The electrocardiosignal of detected person described in Real-time Collection, and generate heart rate variability HRV sequence according to described electrocardiosignal;
Time-domain analysis is carried out to described HRV sequence, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Carry out Time-Frequency Analysis to described HRV sequence, 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 described in real-time judge enters mental fatigue state;
Wherein, described Time-Frequency Analysis is carried out to described HRV sequence, obtains frequency domain character parameter and the time dependent curve of described frequency domain character parameter, comprising:
According to default sample frequency, resampling is carried out to described HRV sequence, 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.
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 whether there is abnormal RR interval between the RR that extracts, if existed, then abnormal RR interval is corrected, 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, then judge that this RR interval is as abnormal RR interval, to the correction of this abnormal RR interval, comprising: described abnormal RR interval is split as 2 RR intervals;
If a RR interval is the half of adjacent R R interval, then judge that this RR interval is as abnormal RR interval, to the correction of this abnormal RR interval, comprising: if continued presence two described abnormal RR interval, then 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, then judge that this RR interval is as outlying interval, to the correction of this abnormal RR interval, comprise: the average calculating described abnormal RR interval adjacent R R interval, is adjusted to described average by described abnormal RR interval.
4. method according to claim 1, is characterized in that, describedly carries out time-domain analysis to described HRV sequence, obtains time domain charactreristic parameter, comprising:
Wherein, N is the total heart beats monitored in preset time period, RR
jfor a jth RR interval,
for the RR interval meansigma methods of N number of heartbeat.
5. method according to claim 1, 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 Δ t and described frequency resolution Δ f met:
6. 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 described in real-time judge enters mental fatigue state, 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, then judge that described detected person enters mental fatigue state.
7. the method according to any one of claim 1 ~ 6, is characterized in that, described frequency domain character parameter also comprises LF/HF ratio, then described method also comprises:
If described detected person enters mental fatigue state, then determine the mental fatigue grade of this detected person according to described LF/HF ratio.
8. the method according to any one of claim 1 ~ 6, is characterized in that, described frequency domain character parameter also comprises LF/HF ratio, then described method also comprises:
Draw the mental fatigue curve of detected person according to described LF/HF ratio, and calculate fatigue curve rate of change according to described mental fatigue curve.
9. 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 the electrocardiosignal of Real-time Collection detected person, and is sent to described master controller;
Described master controller, for generating heart rate variability HRV sequence according to described electrocardiosignal, and controls the mobile processing terminal that described HRV sequence is sent to described mental fatigue monitoring device outside by described wireless transport module;
Described mobile processing terminal comprises:
Receiving element, the heart rate variability HRV sequence that the mental fatigue monitoring device for receiving described mobile processing terminal outside sends;
Time-domain analysis unit, for carrying out time-domain analysis to described HRV sequence, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Frequency-domain analysis unit, for carrying out Time-Frequency Analysis to described HRV sequence, 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, specifically comprise: according to default sample frequency, resampling is carried out to described HRV sequence, form uniform sampling signal, a time window function is determined according to temporal resolution and frequency resolution, short time discrete Fourier transform is adopted to carry out Time-Frequency Analysis to described uniform sampling signal, obtain described frequency domain character parameter and described curve,
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.
10. device according to claim 9, is characterized in that, described monitoring device also comprises loudspeaker arrangement,
Described loudspeaker arrangement is connected with described master controller, and for playing the warning message that described master controller sends, described warning message is sent to described master controller by described mobile processing terminal by described wireless transport module.
11. 1 kinds of mobile processing terminals, is characterized in that, described terminal comprises:
Receiving element, the heart rate variability HRV sequence that the mental fatigue monitoring device for receiving described mobile processing terminal outside sends;
Time-domain analysis unit, for carrying out time-domain analysis to described HRV sequence, obtains time domain charactreristic parameter: all standard deviation SDNN of normal sinus heartbeat RR interval;
Frequency-domain analysis unit, for carrying out Time-Frequency Analysis to described HRV sequence, 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, specifically comprise: according to default sample frequency, resampling is carried out to described HRV sequence, form uniform sampling signal, a time window function is determined according to temporal resolution and frequency resolution, short time discrete Fourier transform is adopted to carry out Time-Frequency Analysis to described uniform sampling signal, obtain described frequency domain character parameter and described curve,
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.
12. terminals according to claim 11, is characterized in that, described frequency domain character parameter also comprises LF/HF ratio, then described terminal also comprises:
Tired level de-termination unit, for when described detected person enters mental fatigue state, determines the mental fatigue grade of this detected person according to described LF/HF ratio.
13. terminals according to claim 11 or 12, it is characterized in that, described frequency domain character parameter also comprises LF/HF ratio, then described terminal also comprises:
Computing unit, for drawing the mental fatigue curve of detected person according to described LF/HF ratio, and calculates fatigue curve rate of change according to described mental fatigue curve.
14. 1 kinds of mental fatigue monitoring systems, is characterized in that, described system comprises: the mental fatigue monitoring device as described in claim 9 or 10 and the mobile processing terminal as described in any one of claim 11 ~ 13.
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Families Citing this family (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104095625A (en) * | 2014-07-05 | 2014-10-15 | 林聪� | Heart rate and electrocardio fatigue measuring instrument |
| CN104161509B (en) * | 2014-08-08 | 2016-01-20 | 申岱 | A kind of heart rate variance analyzing method based on amplitude spectrum and instrument |
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| CN109394197B (en) * | 2018-11-27 | 2022-03-11 | 中山大学 | Heart rate variability measuring method, device and equipment based on time-frequency analysis |
| CN109938719B (en) * | 2019-03-21 | 2023-02-03 | 扬州大学 | Driver fatigue detection method based on physiological parameters |
| CN110141205B (en) * | 2019-05-27 | 2022-03-11 | 深圳市是源医学科技有限公司 | HRV technology-based compression data and fatigue data testing method and device |
| CN110491515A (en) * | 2019-09-25 | 2019-11-22 | 江苏启润科技有限公司 | Driving managing and control system and method based on vehicle-mounted human multi-parameter monitoring terminal |
| CN111920429B (en) * | 2020-09-11 | 2023-04-18 | 深圳市爱都科技有限公司 | Mental stress detection method and device and electronic equipment |
| CN112890818A (en) * | 2021-01-21 | 2021-06-04 | 西安中盛凯新技术发展有限责任公司 | Psychological and autonomic nerve bidirectional assessment system |
| CN113057594A (en) * | 2021-03-19 | 2021-07-02 | 疲劳科学研究(广州)有限公司 | Judgment processing system for fatigue degree evaluation and fatigue judgment method thereof |
| CN113288168A (en) * | 2021-05-21 | 2021-08-24 | 天津工业大学 | Wearable fatigue monitoring of intelligence and early warning system |
| CN116616794B (en) * | 2023-05-18 | 2024-02-20 | 中国人民解放军海军特色医学中心 | Underwater operator fatigue adjustment method and system based on electroencephalogram signals |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1641675A (en) * | 2004-01-07 | 2005-07-20 | 寰硕数码股份有限公司 | Body health harmony data analysing method |
| US7079888B2 (en) * | 2002-04-11 | 2006-07-18 | Ansar, Inc. | Method and apparatus for monitoring the autonomic nervous system using non-stationary spectral analysis of heart rate and respiratory activity |
| CN101455568A (en) * | 2007-12-11 | 2009-06-17 | 卫勇 | Fatigue detection device |
| CN101642368A (en) * | 2008-08-04 | 2010-02-10 | 南京大学 | Method and device for processing autonomic nervous function signals and testing system |
| CN103021134A (en) * | 2012-12-10 | 2013-04-03 | 郭文浩 | Monitoring and alarm device for fatigue driving of automobile |
| CN203138477U (en) * | 2013-03-15 | 2013-08-21 | 北京东方泰华投资有限公司 | Portable electrocardiogram machine |
| CN103479349A (en) * | 2013-09-25 | 2014-01-01 | 深圳市理邦精密仪器股份有限公司 | Electrocardiosignal data acquisition and processing method and system |
-
2014
- 2014-01-22 CN CN201410031167.4A patent/CN103815896B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7079888B2 (en) * | 2002-04-11 | 2006-07-18 | Ansar, Inc. | Method and apparatus for monitoring the autonomic nervous system using non-stationary spectral analysis of heart rate and respiratory activity |
| CN1641675A (en) * | 2004-01-07 | 2005-07-20 | 寰硕数码股份有限公司 | Body health harmony data analysing method |
| CN101455568A (en) * | 2007-12-11 | 2009-06-17 | 卫勇 | Fatigue detection device |
| CN101642368A (en) * | 2008-08-04 | 2010-02-10 | 南京大学 | Method and device for processing autonomic nervous function signals and testing system |
| CN103021134A (en) * | 2012-12-10 | 2013-04-03 | 郭文浩 | Monitoring and alarm device for fatigue driving of automobile |
| CN203138477U (en) * | 2013-03-15 | 2013-08-21 | 北京东方泰华投资有限公司 | Portable electrocardiogram machine |
| CN103479349A (en) * | 2013-09-25 | 2014-01-01 | 深圳市理邦精密仪器股份有限公司 | Electrocardiosignal data acquisition and processing method and system |
Non-Patent Citations (1)
| Title |
|---|
| 《基于心率变异性的精神疲劳的研究》;李延军 等;《中国生物医学工程学报》;20100228;第29卷(第1期);第1-6页 * |
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Address after: Room 2070, building 2, Lane 1800, Xinyang Road, Lingang New Area, China (Shanghai) pilot Free Trade Zone, Pudong New Area, Shanghai, 200120 Patentee after: NEUSOFT XIKANG HEALTHCARE TECHNOLOGY Co.,Ltd. Address before: 100190, 12B01, 03, 05, 07, 09, 9 West Fourth Ring Road, Haidian District, Beijing Patentee before: NEUSOFT XIKANG HEALTHCARE TECHNOLOGY Co.,Ltd. |