CN105976579B - Bad learning state based reminding method and alarm set - Google Patents
Bad learning state based reminding method and alarm set Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1116—Determining posture transitions
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6814—Head
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/7405—Details of notification to user or communication with user or patient ; user input means using sound
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/7455—Details of notification to user or communication with user or patient ; user input means characterised by tactile indication, e.g. vibration or electrical stimulation
Abstract
The present invention provides a kind of bad study habit based reminding method and alarm set, learning state data are counted, the statistic changed with the variation of learning state is calculated, bad learning state judgment threshold is arranged according to statistic, when there is bad learning state, processor triggering is reminded；The learning state include head pose or with eye distance from or with brain situation.The present invention uses statistical method, according to personal habits, establish the judgment criteria with obvious personal feature, and the variation of service condition is tracked in use, constantly self adjustment, adapts to different group and individual character, specific aim is stronger, incremental mode is taken to the change of defective mode, is easy to receive.
Description
Technical field
The present invention relates to electronic technology field more particularly to a kind of bad learning state based reminding methods and alarm set.
Background technique
Often there are some bad habits since control is weak in children in learning process, for example sitting posture is not
Rectify, is not high, close etc. from too with eye distance with brain focus.Sitting posture rectify spirit be easy it is tired out, with the not high influence of brain focus
Learning efficiency, it is close from too with eye distance, it is easy myopia.In learning process, these bad habits are corrected, help the strong of children
Kang Chengchang.Patent CN102743252B " a kind of head-wearing type intelligent eyesight protector ", according to measurement result compared with setting value, such as
Fruit exceeds setting range, then triggers warning device.To different individuals, different, the different people of height of personal preference habit meeting,
Same seat is used in school, with eye distance from also differing.To diversified individual, controlled with the same setting value, it is difficult
To meet individual demand, adaptability is not strong.When correcting bad habit, since personal habits are very different, with doing for single solution for diverse problems
Method, the individual for making starting point low are difficult to adapt to.
Summary of the invention
The object of the present invention is to provide a kind of bad learning state based reminding method and alarm sets, are practised according to Different Individual
It is used, different judgment criterias is established, and with the change of habit, constantly self adjustment.
A kind of one content of present inventive concept, bad learning state based reminding method, counts learning state data,
The statistic changed with the variation of learning state is calculated, bad learning state decision threshold is arranged according to statistic,
When there is bad learning state, processor triggering is reminded；The learning state include head pose or with eye distance from or use
At least one of brain situation.The present invention uses statistical method, according to personal habits, calculates the statistical number with obvious personal feature
Value, as the judgment criteria of bad habit state, in the correction procedure to bad habit, statistic with the change of habit and
Variation, for example, to status data be change from small to big monitoring process (as with eye distance from), statistic with bad habit change
It becomes larger, is monitoring process (such as head skew angle) from large to small to status data, statistic is with bad posture
Change gradually becomes smaller.As the ultimate aim used, user forms correct habit, and statistic no longer changes, and stablizes one
A optimum state.During the change of bad habit, the change situation according to individual is incremental, to different individuals, no
The change speed and process of good habit are that different, final statistic size is also different.
Be arranged according to statistic and remind threshold values, remind threshold values can for statistic or statistic plus or minus
One adjustment numerical value can increase in this way, in statistic and reminding one adaptation area of formation between threshold values to habit change
Adaptability, when statistic reach capacity state no longer change when, the adaptation area become best learning state range.
Before changing bad habit, the distribution center of learning state data is biased to bad habit, and distribution is bigger, with
The change of bad habit, status data center, which is gradually become better, is accustomed to that direction is mobile, and distribution is gradually reduced, and is ultimately at one
A stable kilter region.All learning state data can be used to the statistics of status data, used in order to prevent
Statistic is fallen back in the process, and the sample size of use is answered bigger, can weaken the rollback that abnormal data causes statistic,
Or preliminary screening is carried out to status data, reject obvious abnormal numerical value bigger than normal or less than normal.It is only there are also a kind of statistical method
It is for statistical analysis to the status data for meeting setting condition, such as more than certain after the status data of setting limit value, or sequence
Acquisition data in range, or the status data of fluctuation in a certain range, or non-bad habit is judged as according to statistic
Status data.It according to the defective mode judgment criteria that all data are established, is easier to adapt to, be established according to condition data
Defective mode judgment criteria, it is with strong points.
Another content of present inventive concept, a kind of bad learning state alarm set, including wear-type frame, are placed in frame
Learning state sensor, processor and reminding module on frame, learning state sensor acquire learning state signal transmission processing
Device generates learning state data, it is characterized in that processor counts learning state data, calculates the change with learning state
The statistic changed and changed is arranged bad learning state according to statistic and reminds threshold values；It is mentioned when learning state data exceed
Awake threshold range, processor triggering reminding module are reminded.
It is further to improve, a reset switch can be set, if user feels that the dynamics executed is bigger than normal or less than normal,
It can be zeroed by reset switch, all are since new.Further, it is also possible to a coffret be connected, the note of monitoring process
Record data are transferred to external equipment analysis processing in a wired or wireless fashion.
The present invention uses statistical method, according to personal habits, establishes the judgment criteria with obvious personal feature, and
The variation of service condition is tracked in use process, constantly self adjustment, adapt to different group and individual character, specific aim is stronger, right
Incremental mode is taken in the change of defective mode, is easy to receive.
Detailed description of the invention
Fig. 1 is the configuration block diagram of alarm set.
Fig. 2 is head inclination angle observation circuit connection schematic diagram.
Fig. 3 is with eye distance from observation circuit connection schematic diagram.
Fig. 4 is brain wave observation circuit connection schematic diagram.
Fig. 5 is head inclination angle average statistical flowsheet figure.
Fig. 6 is with eye distance from classified statistic flow chart.
Specific embodiment
Fig. 1 is configuration block diagram of the invention, including learning state sensor, processing controller and alarm set.Study
State sensor acquisition learning state signal transport processor is analyzed and processed, and generates learning state data, and counting statistics
Numerical value, according to statistic be arranged remind threshold values, when status data beyond remind threshold range, processor trigger alarm set into
Row is reminded.Learning state include head pose, with eye distance from, at least one of brain situation；Learning state sensor includes
Head pose sensor, with any one of eye range sensor, eeg sensor；Wherein head pose sensor includes gravity
It is one or more in acceleration transducer or gyroscope or electronic compass；With eye range sensor include infrared ambulator,
Or any one of ultrasonic range finder or laser range finder；Eeg sensor includes single channel or multi-channel sensor, is used
Monopole or bipolar protocol acquire EEG signals；Alarm set includes sound, light, micro-electrical stimulation, low-frequency pulse stimulation, vibration, bone biography
Any one of lead.
Further to improve, memory and data transmission interface can be set in alarm set, and processor is learning state number
According to or the information transmission memory record such as reminder time, number save, record data transmission is set to outside by coffret
Standby, the data transmission interface is wired or wireless way；Wired mode includes any one of RS232 or USB interface, wirelessly
Mode includes any one of bluetooth and radio frequency and zigbee and wifi technology.
Below in the circuit connection diagram of specific embodiment, processor uses ADuC7024, has storage unit, passes through
UART is programmed, and SW1 is power switch, and SW2 and SW3 are reset and lower load switch respectively, shows communication connection in figure, not
Display decoupling and all connections.
For head pose sensor by taking acceleration transducer ADXL345 as an example, circuit connection diagram is as shown in Figure 2.Processing
Device ADuC7024 and digital acceleration sensor ADXL345 is communicated by 4 wire type SPI, and pin 8,9 is to interrupt control.Add
Velocity sensor ADXL345 is placed in head, and for monitoring head pose, when head run-off the straight, gravity is in tri- axis of X, Y, Z
To weight component output signal change, the size of output and 3 axial directions are related with the angle of vertical direction, it is axial with it is perpendicular
Histogram to angle it is smaller, export it is bigger, conversely, output just it is smaller.The weight component that processor is axially exported according to 3
Size can release 3 axial directions and vertical direction angle, to calculate head inclination data.
With eye range sensor by taking infrared distance sensor GP2D12 as an example, circuit connection diagram is as shown in Figure 3.It is infrared
10~80cm of investigative range of distance measuring sensor GP2D12, the analog voltage of 2.55~0.42V of corresponding output, ranging and voltage at
Inverse ratio.The output end vo ut of the analog input end ADC4 and infrared distance sensor GP2D12 of processor ADuC7024 are connected, and connect
Receive the analog signal of infrared distance sensor GP2D12 output.The analog voltage of infrared distance sensor GP2D12 input, through locating
Reason device ADuC7024 internal conversion is counted at digital voltage.
Eeg sensor includes single channel or multi-channel, acquires EEG signals using monopole or bipolar protocol；The single-pass
Road is the EEG signals for only monitoring the one region such as forehead in head, and it is single channel that such as mind, which reads science and technology ThingkGear AM chip,；Institute
Stating multichannel is the EEG signals for monitoring the multiple regions such as head such as forehead, the crown, rear pillow, if Dezhou ADS1299 chip is 8 logical
Road.EEG signals are faint unstable, and are interfered by strong background noise, and eeg sensor, which is passed through, to be enhanced EEG signals and to background
Each wave band EEG signals of the noise reduction process of noise, output can be used directly to do further applied analysis.
Shown in Fig. 4 is that single channel EEG brain wave acquisition sensor ThingkGear AM family chip circuit connection principle is shown
It is intended to.Processor ADuC7024 port P1.5 is connected with eeg sensor ThinkGear AM input terminal RXD, for brain fax
Sensor ThinkGear AM carries out the operation such as initializing.Tri- metal electrodes of A, B, the C contacted with human brain respectively with brain fax sense
The acquisition electrode EEG of device, compare electrode REF and be connected with ground terminal GND, eeg sensor 512 EEG signals numbers of acquisition per second
Strong point, using power spectral analysis method, extract eight wave bands (Delta, Theta, LowAlpha, HighAlpha,
LowBeta, HighBeta, LowGamma, MiddleGamma) power spectrum data and three eSense parameters: focus,
Allowance and blink detecting, are exported by port TXD.
Different-waveband EEG signals reflect different brain states, for example the reflection of Delta (0.5Hz~3Hz) wave is to sleep
Dormancy state, also known as " sound sleep wave ", the reflection of Theta (4Hz~7Hz) wave is sleepy state, also known as " shallowly sleeps wave ", Alpha (7Hz
~13Hz) wave reflection be light state, also known as " loosen wave ", Beta (13Hz~30Hz) wave reflection be consciousness enliven shape
State, also known as " excitation wave ", the reflection of Gamma (30Hz~50Hz) wave is tense situation, also known as " pressure " wave.By based on experiment
The neural algorithm of database carries out analytical calculation using the power spectrum data of different-waveband, can reflect different spiritual shapes
State, such as fall asleep, is sleepy, be absorbed in, loosen, anxiety, pressure, fatigue, excitement, liking, is glad, dejected.It above-mentioned focus and puts
Looseness is exactly to calculate the two kinds of mental state level indexs generated by EEG signals, is numerical value of the range 0~100, is absorbed in
Angle value is bigger, illustrates that focus is higher, otherwise focus is lower；It is bigger to loosen angle value, illustrates more to loosen, conversely, explanation is loosened
It spends lower；In addition, state of mind judgment criteria can also be established according to frequency spectrum data, to mark off the different state of mind.
Electroencephalogramsignal signal analysis method has: 1. time-domain analysis, the geometric properties of Main Analysis EEG waveform, such as amplitude, mean value, variance, skewed
Degree, kurtosis etc.；2. frequency-domain analysis is mainly analyzed using power spectrum, such as power spectral analysis, coherence analysis；3. when
Frequency analysis combines time and frequency and is handled, such as analyzes the matched jamming of sleep spindle.It is herein described to use brain
State include each wave band EEG signals or according to EEG signals calculate the state of mind, the EEG signals include frequency, power,
Amplitude, power spectrum, power spectrum.
The judgement of bad learning state is built upon in the statistical basis to status data, and statistics can be directly to state
Data are counted, and are also possible to count again after sorting to status data, or be grouped statistics to status data, are passed through system
The statistic that meter method obtains includes center value, tagmeme numerical value, group limit value.
Center value is the central tendency level value that the reflection status data that statistics obtains is carried out according to status data,
Represent a kind of habit state, center value includes any one of average, standard deviation, median, mode, wherein average packet
Include simple average, weighted average, harmonic-mean, geometric mean.
Tagmeme numerical value is the data that rear specific position is ranked up to status data, including median, quantile or specific
Any one of positional number.Statistics is ranked up to status data, including is arranged successively according to size or sequence, or press
It is ranked up according to the cumulative frequencies size of status data.Median is ordered into the numerical value on the specific position of data middle, having
Ordinal number is both a kind of position type data and a kind of order statistic according to the equal two parts of quantity, median is divided into, and is gone back simultaneously
It is a kind of estimating for central tendency；Quantile is a kind of positional number of equal part, and median also can be regarded as two quantiles,
It is further that there are also quartiles, eight quantiles etc.；Specific position number can be the positional number of equal part or non-equal part, orderly
Data are divided into the arbitrarily equal or unequal two parts of quantity, for example, the monitoring process of transformation ascending for status data, it can
Descending sequence, tagmeme numerical value can be the average value of n-th maximum value or N number of maximum value；For status data by greatly to
The monitoring process of small transformation ascending can sort, and tagmeme numerical value can be being averaged for n-th minimum value or N number of minimum value
Value.
Status data is grouped, statistics falls into every group of cumulative frequencies, by frequency size to packet sequencing, or presses
It sorts according to class value size, the class value or center value for selecting specific tagmeme are as statistic, and according to the cumulative frequencies fallen into
It is adjusted.Class value is when being grouped statistics to learning state data, and the upper the following group limit value of setting, class mean is a group mediant
Value, grouping can be equidistant grouping, be also possible to different away from grouping.The group limit value of grouping generally sets constant, and falls into every group
Status data cumulative frequencies change with the change of habit, the statistic determined by every group of status data cumulative frequencies
Also can change with the change of habit.Center value includes calculating center value according to all packet datas, reflects all numbers
The center value that this group is calculated according to feature or according to a certain packet data, reflects this group of data characteristics.By taking average value as an example,
Calculation method has: with this group of class mean multiplied by the cumulative frequencies of this group calculate status data and, then just divided by this group of cumulative frequencies
The sum of it is the average value of this group, or counts this group of data, then divided by this group of cumulative frequencies number is exactly this cell mean；With each group
Average value is exactly being averaged for all data multiplied by this group of all status data summation of cumulative frequencies calculating, then divided by total cumulative frequencies
Value.
Illustrate the average statistical method in center value by taking head inclination angle in head pose as an example below.Firstly, when flat
, can be using first status data as statistic in the absence of mean, it, can also be pre- in order to reduce the interference of abnormal data
If a minimum statistics number G (such as 5,10 ...), the average using number of samples greater than G is as statistic, and then statistics
Numerical value is as prompting threshold values.The change process of head inclination angle bad habit be head skew angle from large to small, to be greater than remind
The attitude data of threshold values triggers alarm set, if the LED light in Fig. 2 brightens, user is promoted to correct head skew angle.With
The change of head pose defective mode, statistic gradually become smaller.If user forms correct sitting posture habit, statistic
No longer change.In addition it is also possible to which (such as 3,5 degree) of offset Δ that increase an adjustment on the basis of statistic are used as and remind
Threshold values triggers alarm set to the attitude data for reminding threshold values is greater than, in this way, being formed between statistic and prompting threshold values
One adaptation area can increase the adaptability changed to habit.When statistic reach capacity state no longer change when, the adaptation
Area becomes best head pose range.
When statistics number N is excessive, behind influence of the sample to statistical result gradually become smaller, a maximum statistics can be limited
Number E (such as 1000), when statistics number N be greater than limit value E, it is constant to be fixed as E, or assign respectively to average value and subsequent samples
One weighted value such as 0.9 and 0.1 calculates weighted average as statistic.If subsequent statistic becomes larger, keep
Preceding statistic is constant, i.e., statistic, which only becomes smaller, does not become larger, and avoids the fluctuation for reminding threshold values, prevents a retroversion for appearance state.
Further, it is also possible to carry out condition statistics to status data, selection meets the status data to impose a condition and is counted,
If only reminded the attitude data of threshold values or statistic to count to being less than, calculate average, and to be greater than remind threshold values or
The attitude data of statistic does not count；By conditional statistics, statistic can be accelerated and become smaller, improve what habit changed
Speed.
Head inclination angle average statistical flowsheet as shown in figure 5, specific method steps are as follows:
System initialization is carried out after<step a1>booting, to minimum statistics number G, maximum statistics number limit value E and tune
Integer value Δ carries out assignment, into next step；
<step a2>acquires acceleration information, calculates head pose D, into next step；
<step a3>judge statistics number whether N < G, if so, counting statistics times N=N+1, attitude data and S=K
× (N-1)+D, average value K=S/N are transferred to step a2, if not, entering in next step；
<step a4>judge inclination data whether D > F, if so, triggering remind, step a2 is transferred to, if not, under
One step；<step a5>counting statistics times N=N+1, attitude data and S=K × (N-1)+D calculate average value K=S/N, enter
In next step；
<step a6>, which is calculated, reminds threshold values F=K+ Δ, into next step；
Does<step a7>judge statistics number N>=E if so, enabling N=E, it is transferred to step a2, if not, being transferred to step a2.
Maximum prompting number can be set in prompting, when being greater than maximum prompting number, terminated under this bad habit state
Prompting movement, reminded again when being transferred to bad habit state again.
Illustrate tagmeme numerical statistic method for brain situation below, more specifically to use brain focus in the state of mind
For be illustrated, the EEG signals of the equally applicable different-waveband of the method for the present invention.Firstly, arranging with brain focus data
Sequence counts median, and the median using number of samples greater than G is that focus is relatively low with the defective mode of brain as statistic,
The culture of good habit is that focus is got higher by low, can reduce by an adjustment value, Δ on the basis of statistic (as united
Count value 5%, 10%... or 5,10...) as threshold values is reminded, to enhance the adaptability that overcomes to bad habit.If adopted
Use lower quartile numerical value as statistic, then it can be directly using statistic as prompting threshold values.It is of course also possible to select one
A specific position number selects a specific position number or selection the as statistic such as between median and lower quartile
N number of maximum value etc., further, it is also possible to select the average value of the focus data of specific sequence section as statistic.
Further, it is also possible to carry out condition statistics to focus data, the special of threshold values or statistic only such as is reminded to being greater than
Note degree reminds the focus data of threshold values or statistic not count according to being counted to being less than；It in the ranking, can be
Equal focus data in chronological sequence sort, and adjacent data can be equal, equal focus data can also be made
It is ranked up for a data, adjacent data differ.
Illustrate classified statistic method below.Sequence is grouped to focus data, such as at interval of 10 one point of settings
0~100 focus data are divided 10 groups by group, add up the focus data frequency for falling into each group respectively, further according to accumulative frequency
Number size is ranked up grouping, on the basis of sorting herein, selects the class mean of a setting tagmeme as statistic, when special
Degree is infused according to the statistic is less than, is judged as bad and uses brain state.
For another example for the Beta wave power modal data in brain state, magnitude range is 0~20 (× 105μν2),
5,10,15 3 grouping values are set, four kinds of different state of mind can be divided, it is the variation that grows from weak to strong that Beta wave, which monitors process,
I.e. Beta wave illustrates more by force study thinking actively, in packet by packet basis, replaces falling into each group Beta wave size of data with class mean,
Center value is calculated according to cumulative frequencies in conjunction with each group Beta wave number, for example, according to the product of the cumulative frequencies of each group and class mean
The sum of, calculate Beta wave number according to average divided by discussing for total cumulative frequencies, when occur being less than the Beta wave numbers of mean values according to when,
It is judged as bad and uses brain state.
With eye distance from classified statistic process as shown in fig. 6, selection the maximum packet data counting statistics numerical value of cumulative frequencies,
Steps are as follows for specific method:
System initialization is carried out after<step b1>booting, to minimum statistics number G, adjustment value, Δ assignment, setting grouping
Range Ai~Bi, into next step；
<step b2>collection voltages signal, is converted into voltage data D, into next step；
Does<step b3>judge largest cumulative frequency Ni > G if so, entering in next step, if not, being transferred to step b5；
Does<step b4>judge voltage data D > F if so, triggering is reminded, it is transferred to step b2, if not, into next
Step；
<step b5>judges that current data falls into i-th group, adds up this group of times N i=Ni+1, into next step；
<step b6>judges whether i-th group of cumulative number Ni is maximum, if so, entering in next step, if not, being transferred to step
Rapid b2；
<step b7>takes i-th group of class mean as statistic K=(Ai+Bi)/2, enables and reminds threshold values F=K+ Δ, turns
To step b2.
According to the voltage-of GP2D12 apart from curve of output, between 10-50cm corresponding voltage data be 2.55~
0.61v is group away from being divided into 8 groups, i.e. (i=1,2,3 ... 8): 2.55v < N1≤1.79v, 1.79v by Ai < Ni≤Bi using 5cm
< N2≤1.40v, 1.40v < N3≤1.18v, 1.18v < N4≤0.99v, 0.99v < N5≤0.88v, 0.88v < N6≤
0.78v, 0.78v < N7≤0.69v, 0.69v < N8≤0.61v are counted respectively by above-mentioned grouping and are fallen into every group of cumulative frequencies,
With eye distance from monitoring process be with a distance from ascending variation, voltage be it is descending, cumulative frequencies maximum, and be greater than default
The packet data center value or class mean (Ai+Bi)/2 or class value (Ai, Bi) of value G (such as 50,100 ...) is used as statistic,
Increase an adjustment value, Δ the 5% of such as statistic (, 10%...) on the basis of statistic as reminding threshold values, with
Enhance the adaptability overcome to bad habit.In addition it is also possible to upper one group of the class value for selecting the group neighbouring is as statistic,
It is directly set as reminding threshold values.
Statistic processes is divided into two statistics stages by statistics number G by the above method, is little in sampling number first
In setting the unconditional statistics under number, followed by it is greater than the statistics of having ready conditions under setting number in sampling number.Pass through sampling
Number is established no more than the acquisition data under setting number judges the statistic of bad eye condition, to understand user currently
It is accustomed to eye；It is accustomed to the statistic of foundation with eye according to individual, participates in what statistics calculated in reverse as subsequent sampling data
On the one hand condition can fall into the relatively large grouping of voltage data to avoid statistic, cause the room for manoeuvre of judgment criteria, another party
Face can make voltage statistic numerical value become smaller, and improvement is accustomed to eye, progressively reach personal eye distance from optimum state.
The present invention is that basis establishes bad habit judgment criteria to the statistical method of status data, right through the foregoing embodiment
Method of the invention is expounded, and should not be construed as the limitation to the application.This method is not limited to learning state, same suitable
For other work, living scenes that can establish judgment criteria by statistical method.
Claims (9)
1. bad learning state is arranged by the statistical analysis to learning state data in a kind of bad learning state based reminding method
Remind threshold values, when there is bad learning state, triggering prompting, feature the following steps are included:
(1) learning state data are acquired, the statistic of learning state is calculated according to sampled data；
(2) according to the statistic, bad learning state is set and reminds threshold values；
(3) the monitoring process changed from small to big to learning state data is triggered if the sampled data is less than and reminds threshold values and is mentioned
Wake up, the sampled data not as learning state statistical data, if the sampled data be greater than remind threshold values, the hits
According to as statistical data, the learning state statistic is calculated, bad learning state is updated and reminds threshold values；Alternatively, to study
The monitoring process of status data from large to small triggers prompting if the sampled data is greater than and reminds threshold values, the sampled data
Not as the statistical data of learning state, if the sampled data, which is less than, reminds threshold values, using the sampled data as statistical number
According to calculating the learning state statistic, update bad learning state and remind threshold values.
2. based reminding method according to claim 1, it is characterized in that: the statistic includes being united according to learning state data
Count the center value obtained.
3. based reminding method according to claim 2, it is characterized in that: the center value includes average, standard deviation, middle position
Any one of number, mode.
4. based reminding method according to claim 1, it is characterized in that: the statistic include according to learning state data into
Tagmeme numerical value after row sequence.
5. based reminding method according to claim 4, it is characterized in that: the sequence include according to learning state size of data or
Cumulative frequencies are ranked up.
6. based reminding method according to claim 4, it is characterized in that: the tagmeme numerical value include median, it is quantile, specific
Any one of positional number.
7. based reminding method according to claim 1, it is characterized in that: the statistics includes being grouped to learning state data
Statistics, the statistic include by the center value of classified statistic result calculating, tagmeme numerical value, group limit value, class mean
It is any.
8. based reminding method according to claim 7, it is characterized in that: the center value includes the center of all packet datas
Center value in numerical value or a certain grouping.
9. a kind of bad learning state alarm set, including wear-type frame, the learning state sensor being placed on frame are handled
Device and reminding module, learning state sensor acquire the processing analysis of learning state signal transport processor, it is characterized in that:
(1) learning state data are acquired by learning state sensor, processor calculates the system of learning state according to sampled data
Count value is arranged bad learning state and reminds threshold values according to the statistic；
(2) the monitoring process changed from small to big to learning state data, if the sampled data, which is less than, reminds threshold values, processor touching
Reminding module is sent out, which locates not as the statistical data of learning state if the sampled data, which is greater than, reminds threshold values
Device is managed using the sampled data as statistical data, calculates the learning state statistic, bad learning state is updated and reminds valve
Value；Alternatively, the monitoring process to learning state data from large to small, if the sampled data, which is greater than, reminds threshold values, processor
Trigger reminding module, the sampled data not as learning state statistical data, if the sampled data be less than remind threshold values,
Processor calculates the learning state statistic using the sampled data as statistical data, updates bad learning state and reminds
Threshold values.
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| CN110101385A (en) * | 2019-05-09 | 2019-08-09 | 吉林大学 | A kind of intelligent military helmet and monitoring stimulating method of enhancing soldier's function of human body |
| CN110599748B (en) * | 2019-09-20 | 2021-05-11 | 英华达（上海）科技有限公司 | Reminding method and reminding device applying same |
| CN113558614A (en) * | 2021-08-31 | 2021-10-29 | 中山优感科技有限公司 | Intelligent detection system and method for healthy sitting posture |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102743252A (en) * | 2012-06-04 | 2012-10-24 | 南京智启软件开发有限公司 | Intelligent head-wearing eyesight protection instrument |
| DE102011122572A1 (en) * | 2011-04-29 | 2012-10-31 | Horst Linzmeier | Control device for controlling human body posture fault, has signal transmitter to transmit specific signal, when human body surface curvature aspect ratio measured by measuring portion is different from predetermined value |
| CN102973276A (en) * | 2012-12-20 | 2013-03-20 | 毛毅翔 | Device for preventing myopia and cervical spondylosis |
-
2016
- 2016-03-10 CN CN201610136628.3A patent/CN105976579B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102011122572A1 (en) * | 2011-04-29 | 2012-10-31 | Horst Linzmeier | Control device for controlling human body posture fault, has signal transmitter to transmit specific signal, when human body surface curvature aspect ratio measured by measuring portion is different from predetermined value |
| CN102743252A (en) * | 2012-06-04 | 2012-10-24 | 南京智启软件开发有限公司 | Intelligent head-wearing eyesight protection instrument |
| CN102973276A (en) * | 2012-12-20 | 2013-03-20 | 毛毅翔 | Device for preventing myopia and cervical spondylosis |
Non-Patent Citations (1)
| Title |
|---|
| "利用3轴数字加速度计实现功能全面的计步器设计";Neil Zhao;《Analog Dialogue》;20100630;论文正文第2页 * |
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