CN105976579A - Bad learning state reminding method and reminding device - Google Patents
Bad learning state reminding method and reminding device 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 invention provides a bad learning habit reminding method and reminding device. Learning state data are counted. A statistical value which changes as learning state changes is calculated. According to the statistical value, a bad learning state judging threshold value is set. In a bad learning state, a processor triggers a reminder. The learning state comprises a head posture, an eye distance, or a brain condition. According to the invention, a statistical method is used; according to personal habits, a judgment standard with obvious individual characteristics is established; the change of usage is tracked in the course of using; constant self-adjusting is carried out to adapt to different groups and individualities; and the bad learning habit reminding method and reminding device are targeted, take a gradual approach for bad state changes, and are easy to accept.
Description
Technical field
The present invention relates to electronic technology field, particularly relate to a kind of bad learning state based reminding method and alarm set.
Background technology
Children are weak due to control, learning process often occurs some bad habits, such as sitting posture are not rectified, focus of requiring mental skill is the highest, with eye distance close to too etc..It is the most tired out that sitting posture does not rectify spirit, and focus of requiring mental skill is the highest affects the learning efficiency, with eye distance close to too, and easily myopia.In learning process, correct these bad habits, help growing up healthy and sound of children.Patent CN102743252B " a kind of head-wearing type intelligent eyesight protector ", compares with setting value according to measurement result, if beyond set point, then triggering alarm device.To different individualities, individual's preference custom can be different, and the people that height is different uses same seat in school, and it uses eye distance from also.To diversified individuality, being controlled by same setting value, it is difficult to meet individual demand, adaptability is the strongest.When correcting bad habit, owing to personal habits is very different, by the way imposed uniformity without examining individual cases, the individuality making starting point low is difficult in adapt to.
Summary of the invention
It is an object of the invention to provide a kind of bad learning state based reminding method and alarm set, be accustomed to according to Different Individual, set up different criterions, and along with the change of custom, constantly oneself adjusts.
One content of present inventive concept, a kind of bad learning state based reminding method, learning state data are added up, calculate the statistic changed along with the change of learning state, according to statistic, bad learning state judgment threshold is set, when there is bad learning state, processor triggers to be reminded；Described learning state include head pose or with eye distance from or situation of requiring mental skill at least one.The present invention uses statistical method, according to personal habits, calculate the statistic with obvious personal feature, criterion as bad habit state, in the correction procedure to bad habit, statistic changes with the change of custom, such as, to status data be change from small to big monitoring process (as with eye distance from), statistic becomes larger with the change of bad habit, being monitoring process (such as head skew angle) from large to small to status data, statistic tapers into the change of bad attitude.As the ultimate aim used, user forms correct custom, and statistic no longer changes, stable at an optimum state.During the change of bad habit, incremental according to individual change situation, to different individualities, the change speed of bad habit and process are different, and final statistic size is the most different.
According to statistic, prompting threshold value is set, prompting threshold value can be statistic, or statistic adds or deducts one and adjusts numerical value, so, between statistic and prompting threshold value, form one adapt to district, can increase to custom change adaptability, when statistic reach capacity state no longer change time, this adaptation district becomes optimal learning state scope.
Before changing bad habit, distribution center's deflection bad habit of learning state data, distribution is bigger, change along with bad habit, status data center gradually become better custom direction move, distribution is gradually reduced, and is ultimately at a stable kilter region.The statistics of status data can be used all of learning state data, in order to prevent statistic during using from falling back, the sample size used should be bigger, abnormal data can be weakened and cause the rollback of statistic, or status data is carried out Preliminary screening, reject abnormal numerical value bigger than normal or less than normal.Also has a kind of statistical method, it is only the satisfied status data imposed a condition to be carried out statistical analysis, such as more than the status data setting limit value, or a range of collection data after sequence, or the status data that fluctuation is within the specific limits, or it is judged as the status data of non-bad habit according to statistic.The defective mode criterion set up according to all data, is easier to adapt to, the defective mode criterion set up according to condition data, with strong points.
Another content of present inventive concept, a kind of bad learning state alarm set, including wear-type framework, learning state sensor, processor and the prompting module being placed on framework, learning state sensor acquisition learning state signal transport processor generates learning state data, it is characterized in that learning state data are added up by processor, calculate the statistic changed along with the change of learning state, bad learning state is set according to statistic and reminds threshold value；When learning state data are beyond reminding threshold range, and processor triggers prompting module and reminds.
Further improving, can arrange a reset switch, if the dynamics that user sensation performs is bigger than normal or less than normal, can be made zero by reset switch, all are from the beginning of newly.Further, it is also possible to connect a coffret, the record data of monitoring process are transferred to external equipment analyzing and processing in a wired or wireless fashion.
The present invention uses statistical method, according to personal habits, set up the criterion with obvious personal feature, and in use follow the tracks of the change of service condition, constantly oneself adjusts, and adapts to different colonies and individual character, and specific aim is higher, incremental mode is taked in change to defective mode, it is easy to accept.
Accompanying drawing explanation
Fig. 1 is the configuration block diagram of alarm set.
Fig. 2 is head inclination angle observation circuit connection diagram.
Fig. 3 is from observation circuit connection diagram with eye distance.
Fig. 4 is brain wave observation circuit connection diagram.
Fig. 5 is head inclination angle average statistical flowsheet figure.
Fig. 6 is from classified statistics flow chart with eye distance.
Detailed description of the invention
Fig. 1 is the configuration block diagram of the present invention, including learning state sensor, processing controller and alarm set.Learning state sensor acquisition learning state signal transport processor is analyzed processing, generate learning state data, and counting statistics numerical value, prompting threshold value is set according to statistic, when status data is beyond reminding threshold range, and processor triggers alarm set and reminds.Learning state include head pose, with eye distance from, require mental skill in situation at least one；Learning state sensor includes head pose sensor, uses any one in eye range sensor, brain electric transducer；During wherein head pose sensor includes Gravity accelerometer or gyroscope or electronic compass one or more；Any one in infrared ambulator or ultrasonic range finder or laser range finder is included with eye range sensor；Brain electric transducer includes single channel or multichannel sensor, uses one pole or bipolar protocol to gather EEG signals；Alarm set includes any one in the stimulation of sound, light, micro-electrical stimulation, low-frequency pulse, vibrations, bone conduction.
Further improve, alarm set can arrange memorizer and data transmission interface, processor transmits memorizer record learning state data or the information such as reminder time, number of times and preserves, by coffret, record data being sent to external equipment, described data transmission interface is wired or wireless mode；Wired mode includes any one in RS232 or USB interface, and wireless mode includes any one in bluetooth and radio frequency and zigbee and wifi technology.
In detail below in the circuit connection diagram of embodiment, processor uses ADuC7024, has memory element, being programmed by UART, SW1 is on and off switch, SW2 and SW3 is to reset and lower load switch respectively, figure shows communication connection, does not shows decoupling and all connections.
Head pose sensor is as a example by acceleration transducer ADXL345, and circuit connection diagram is as shown in Figure 2.Processor ADuC7024 and digital acceleration sensor ADXL345 is communicated by 4 wire types SPI, and pin 8,9 is to interrupt control.Acceleration transducer ADXL345 is placed in head, it is used for monitoring head pose, when head run-off the straight, gravity changes in the weight component output signal that X, Y, Z tri-is axial, the size of output is the most relevant with the angle of vertical direction with 3, and the least with the angle of vertical direction, its output is the biggest, otherwise, export the least.Processor, according to the weight component size of 3 axial outputs, can be released 3 axial and vertical direction angles, thus calculate head inclination data.
With eye range sensor as a example by infrared distance sensor GP2D12, circuit connection diagram is as shown in Figure 3.The investigative range 10～80cm of infrared distance sensor GP2D12, the analog voltage of corresponding output 2.55～0.42V, range finding is inversely proportional to voltage.The output end vo ut of the analog input end ADC4 and infrared distance sensor GP2D12 of processor ADuC7024 is connected, and receives the analogue signal of infrared distance sensor GP2D12 output.The analog voltage of infrared distance sensor GP2D12 input, treated device ADuC7024 internal conversion becomes digital voltage to add up.
Brain electric transducer includes single channel or multichannel, uses one pole or bipolar protocol to gather EEG signals；Described single channel is the EEG signals only monitoring one region such as forehead of head, is single channel as god reads science and technology ThingkGear AM chip；Described multichannel is the EEG signals in multiple regions such as monitoring head such as forehead, the crown, rear pillow, if Dezhou ADS1299 chip is 8 passages.The faint instability of EEG signals, is disturbed by strong background noise again, and brain electric transducer is through the noise reduction process strengthened EEG signals and to background noise, and each wave band EEG signals of output can be used directly to do further applied analysis.
Shown in Fig. 4 is single channel EEG brain wave acquisition sensor ThingkGear AM family chip circuit catenation principle schematic diagram.Processor ADuC7024 port P1.5 is connected with brain electric transducer ThinkGear AM input RXD, for brain electric transducer ThinkGear AM carries out the operations such as initialization.Tri-metal electrodes of A, B, the C contacted with human brain respectively with the acquisition electrode EEG of brain electric transducer, compare electrode REF and earth terminal GND and be connected, 512 EEG signals data points of brain electric transducer collection per second, use power spectral analysis method, extract the power spectrum data of eight wave bands (Delta, Theta, LowAlpha, HighAlpha, LowBeta, HighBeta, LowGamma, MiddleGamma), with three eSense parameters: focus, allowance and nictation are detected, and are exported by port TXD.
The brain states that the reflection of different-waveband EEG signals is different, the reflection of such as Delta (0.5Hz～3Hz) ripple is sleep state, also known as " sound sleep ripple ", the reflection of Theta (4Hz～7Hz) ripple is sleepy state, also known as " shallow sleep ripple ", the reflection of Alpha (7Hz～13Hz) ripple is light state, also known as " loosening ripple ", the reflection of Beta (13Hz～30Hz) ripple is consciousness active state, also known as " excitation wave ", the reflection of Gamma (30Hz～50Hz) ripple is tense situation, also known as " pressure " ripple.By neural algorithm based on experimental data base, use the power spectrum data of different-waveband to be analyzed calculating, the different mental status can be reflected, as fallen asleep, sleepy, be absorbed in, loosen, anxiety, pressure, fatigue, excitement, like, glad, dejected etc..Above-mentioned focus and allowance are through EEG signals and calculate the two kinds of mental state level indexs generated, be scope 0～100 numerical value, absorbed angle value is the biggest, illustrates that focus is the highest, otherwise focus is the lowest；Loosen angle value the biggest, illustrate more to loosen, otherwise, illustrate that allowance is the lowest；It addition, mental status criterion can also be set up according to frequency spectrum data, thus mark off the different mental status.Electroencephalogramsignal signal analysis method has: 1. time-domain analysis, the geometric properties of Main Analysis EEG waveform, such as amplitude, average, variance, skewed degree, kurtosis etc.；2. frequency-domain analysis, is analyzed mainly by power spectrum, such as power spectral analysis, coherence analysis etc.；3. time frequency analysis, combines time and frequency and processes, such as the matched jamming analysis etc. to sleep spindle.The herein described state of requiring mental skill includes each wave band EEG signals or the mental status calculated according to EEG signals, and described EEG signals includes frequency, power, amplitude, power spectrum, power spectrum.
The judgement of bad learning state is built upon in the statistical basis to status data, statistics can be directly to add up status data, can also be to add up again after status data sequence, or status data is carried out classified statistics, the statistic drawn by statistical method includes center value, tagmeme numerical value, group limit value.
Center value is to carry out adding up the central tendency level value of the reflection status data drawn according to status data, represent a kind of custom state, center value includes any one in average, standard deviation, median, mode, and wherein average includes simple average, weighted mean, harmonic mean, geometric mean.
Tagmeme numerical value is the data that status data is ranked up rear ad-hoc location, including any one in median, quantile or ad-hoc location number.Status data is ranked up statistics, is arranged in order including according to size or sequence, or 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, and ordered data is divided into two parts that quantity is equal, and median is a kind of position type data, is again a kind of order statistic, simultaneously or the estimating of a kind of central tendency；Quantile is the positional number of a kind of decile, and median can also regard two quantiles, the most also quartile, eight quantiles etc. as；Ad-hoc location number can be decile or the positional number of non-decile, ordered data is divided into two parts that quantity is the most equal or different, such as, the monitoring process of transformation ascending for status data, can descending sort, tagmeme numerical value can be n-th maximum, or the meansigma methods of N number of maximum；The monitoring process of transformation descending for status data, can ascending sort, and tagmeme numerical value can be n-th minima, or the meansigma methods of N number of minima.
Being grouped status data, statistics falls into the cumulative frequencies often organized, and by frequency size to packet sequencing, or sorts according to class value size, selects the class value of specific tagmeme or center value as statistic, and is adjusted according to the cumulative frequencies fallen into.When class value is that learning state data are carried out classified statistics, the group limit value up and down of setting, midvalue of class is group intermediate value, and packet can be equidistant packet, it is also possible to be different away from packet.The group limit value of packet typically sets constant, and falls into the status data cumulative frequencies often organized and change along with the change of custom, and the statistic determined by the status data cumulative frequencies often organized is changed as well as the change of custom.Center value includes calculating center value according to all grouped datas, reflects all data characteristicses or calculates the center value of this group according to a certain grouped data, reflecting this group data characteristics.As a example by meansigma methods, computational methods have: with this group midvalue of class be multiplied by this group cumulative frequencies calculate status data and, it is exactly the meansigma methods of this group again divided by this group cumulative frequencies, or adds up this group data sum, then be exactly this cell mean divided by this group cumulative frequencies number；It is multiplied by this group cumulative frequencies with each cell mean and calculates all status data summations, then be exactly the meansigma methods of all data divided by total cumulative frequencies.
Average statistical method in center value is described as a example by head inclination angle below in head pose.First, when average not in the presence of, can be using first status data as statistic, in order to reduce the interference of abnormal data, minimum statistics number G (such as 5,10 ...) can also be preset, using the number of samples average more than G as statistic, and then using statistic as reminding threshold value.The change process of head inclination angle bad habit be head skew angle from large to small, to more than remind threshold value attitude data, trigger alarm set, as the LED in Fig. 2 brightens, promote user correct head skew angle.Along with the change of head pose defective mode, statistic tapers into.If user forms correct sitting posture custom, statistic no longer changes.In addition, an offset Δ (such as 3,5 degree) adjusted can also be increased as reminding threshold value on the basis of statistic, to more than the attitude data reminding threshold value, trigger alarm set, so, between statistic and prompting threshold value, form one adapt to district, the adaptability that custom is changed can be increased.When statistic reach capacity state no longer change time, this adaptation district becomes optimal head pose scope.
When statistics number N is excessive, the impact of statistical result is tapered into by sample below, maximum statistics number E (such as 1000) can be limited, when statistics number N is more than limit value E, it is fixed as E constant, or meansigma methods and subsequent samples are given respectively a weighted value such as 0.9 and 0.1, calculate weighted mean as statistic.If follow-up statistic becomes big, then before keeping, statistic is constant, i.e. statistic only diminish constant greatly, it is to avoid remind the fluctuation of threshold value, prevent falling back of an appearance state.
In addition, status data can also be carried out condition statistics, select to meet the status data imposed a condition and add up, as only added up less than the attitude data reminding threshold value or statistic, calculate average, and do not add up more than the attitude data reminding threshold value or statistic；By statistics with good conditionsi, statistic can be accelerated and diminish, improve the speed that custom changes.
Head inclination angle average statistical flowsheet is as it is shown in figure 5, concrete grammar step is as follows:
Carry out system initialization after<step a1>start, minimum statistics number G, maximum statistics number limit value E and adjustment value, Δ are carried out assignment, enters next step；
<step a2>gathers acceleration information, calculates head pose D, enters next step；
<step a3>judges statistics number whether N ＜ G, if it is, counting statistics times N=N+1, attitude data and S=K × (N-1)+D, meansigma methods K=S/N, proceeds to step a2, if it does not, enter next step；
<step a4>judges inclination data whether D ＞ F, reminds if it is, trigger, proceeds to step a2, if it does not, enter next step；
<step a5>counting statistics times N=N+1, attitude data and S=K × (N-1)+D, calculate meansigma methods K=S/N, enter next step；
<step a6>calculates and reminds threshold value F=K+ Δ, enters next step；
<step a7>judges statistics number N>=E？If it is, make N=E, proceed to step 2, if it does not, proceed to step a2.
Prompting can set maximum prompting number of times, when reminding number of times more than maximum, terminate the prompting action under this bad habit state, remind again when again proceeding to bad habit state.
Illustrate as a example by tagmeme numerical statistic method, the more specifically focus of requiring mental skill in the mental status are described below as a example by the situation of requiring mental skill, the EEG signals of the equally applicable different-waveband of the inventive method.First, to requiring mental skill, focus data are ranked up, statistics median, using the number of samples median more than G as statistic, the defective mode required mental skill is that focus is on the low side, the cultivation of good habit is that focus is uprised by low, can reduce one and adjust value, Δ (such as 5%, 10%... or 5, the 10... of statistic) as prompting threshold value, the adaptability overcome bad habit with enhancing on the basis of statistic.If use lower quartile numerical value as statistic, then can directly using statistic as remind threshold value.Certainly, an ad-hoc location number can also be selected as statistic, as selected an ad-hoc location number between median and lower quartile or selecting n-th maximum etc., further, it is also possible to select the meansigma methods of the focus data of specific sequence section as statistic.
Further, it is also possible to focus data are carried out condition statistics, as only added up more than the focus data reminding threshold value or statistic, and do not add up less than the focus data reminding threshold value or statistic；In the ranking, equal focus data in chronological sequence can be sorted, adjacent data can be equal, it is also possible to equal focus data is ranked up as data, adjacent data.
Explanation classified statistics method below.nullVoltage distances curve of output according to GP2D12，Between 10-50cm, corresponding voltage data are 2.55～0.61v，With 5cm for group away from，It is divided into 8 groups，I.e. Ai ＜ Ni≤Bi (i=1,2,3 ... 8): 2.55v ＜ N1≤1.79v、1.79v ＜ 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，Add up respectively by above-mentioned packet and fall into the cumulative frequencies often organized，With eye distance from monitoring process be apart from ascending change，Voltage is descending，Cumulative frequencies is maximum，And more than preset value G (such as 50、100 ...) grouped data center value or midvalue of class (Ai+Bi)/2 or class value (Ai、Bi) as statistic，On the basis of statistic, increase by one adjust value, Δ (5% such as statistic、10%...) as reminding threshold value，Adaptability bad habit overcome with enhancing.In addition it is also possible to select the class value of this group neighbouring upper a group as statistic, directly it is set to remind threshold value.
Focus data are carried out packet sequencing, such as set a packet at interval of 10,0～100 focus data are divided 10 groups, the accumulative focus data frequency falling into each group respectively, further according to cumulative frequencies size, packet is ranked up, at this on the basis of sequence, selects a midvalue of class setting tagmeme as statistic, when focus data are less than this statistic, it is judged that for bad state of requiring mental skill.
For another example as a example by the Beta wave power modal data in the state of requiring mental skill, its magnitude range is 0～20 (× 105μν2), 5,10,15 3 packet values are set, four kinds of different mental status can be divided, Beta ripple monitoring process is the change that grows from weak to strong, i.e. Beta ripple illustrates more by force study thinking actively, in packet by packet basis, replace falling into each group of Beta wave datum size with midvalue of class, center value is calculated in conjunction with each group of Beta wave datum cumulative frequencies, such as, according to the sum of products of the cumulative frequencies of each group Yu midvalue of class, Beta wave datum average is calculated divided by discussing of total cumulative frequencies, when the Beta wave datum less than mean values occurs, it is judged that for bad state of requiring mental skill.
With eye distance from classified statistics flow process as shown in Figure 6, selecting the grouped data counting statistics numerical value that cumulative frequencies is maximum, concrete grammar step is as follows:
Carry out system initialization after<step b1>start, to minimum statistics number G, adjust value, Δ assignment, packet scope Ai～Bi are set, enter next step；
<step b2>gathers voltage signal, is converted into voltage data D, enters next step；
<step b3>judges largest cumulative frequency Ni ＞ G？If it is, enter next step, if it does not, proceed to step b5；
<step b4>judges voltage data D ＞ F？Remind if it is, trigger, proceed to step b2, if it does not, enter next step；
<step b5>judges that current data falls into i-th group, accumulative this group times N i=Ni+1, enters next step；
<step b6>judges that the cumulative number Ni of i-th group is the most maximum, if it is, enter next step, if it does not, proceed to step b2；
<step b7>takes the midvalue of class of i-th group and reminds threshold value F=K+ Δ as statistic K=(Ai+Bi)/2, order, turn to step b2.
Above-mentioned is that status data is carried out condition statistics, only carries out classified statistics to less than the voltage data reminding threshold value or statistic, and is more than triggering prompting and reminds the voltage data of threshold value or statistic not add up；By statistics with good conditionsi, can accelerate statistic and diminish, improve the speed that custom changes, that avoids statistic falls into the packet that voltage data is relatively large, causes the room for manoeuvre of criterion simultaneously.
The present invention is that the statistical method of status data is set up bad habit criterion by basis, is set forth the method for the present invention by above-described embodiment, should not be construed as the restriction to the application.This method is not limited to learning state, and being equally applicable to other can set up the work of criterion, living scene by statistical method.
Claims (10)
1. a bad learning state based reminding method, by the statistical analysis to learning state data, arranges bad learning state and reminds threshold value, and when there is bad learning state, processor triggers to be reminded, and its feature comprises the following steps:
(1) learning state data are added up by processor, calculate the statistic changed along with the change of learning state；
(2) according to described statistic, bad learning state is set and reminds threshold value；
(3) when described learning state data are beyond reminding threshold range to trigger prompting.
Based reminding method the most according to claim 1, is characterized in that: described statistic includes the center value drawn according to learning state data statistics.
Based reminding method the most according to claim 2, is characterized in that: described center value includes any one in average, standard deviation, median, mode.
Based reminding method the most according to claim 1, is characterized in that: described statistic includes the tagmeme numerical value after being ranked up according to learning state data.
Based reminding method the most according to claim 4, is characterized in that: described sequence includes being ranked up according to learning state size of data or cumulative frequencies.
Based reminding method the most according to claim 4, is characterized in that: described tagmeme numerical value includes any one in median, quantile, ad-hoc location number.
Based reminding method the most according to claim 1, is characterized in that: described statistics includes carrying out learning state data classified statistics, and described statistic includes any one in the center value by the calculating of classified statistics result, tagmeme numerical value, group limit value, midvalue of class.
Based reminding method the most according to claim 7, is characterized in that: described center value includes the center value in the center value of all grouped datas or a certain packet.
Based reminding method the most according to claim 1, is characterized in that: described statistics includes adding up the satisfied learning state data imposed a condition, or adds up global learning status data.
10. a bad learning state alarm set, including wear-type framework, learning state sensor, processor and the prompting module being placed on framework, learning state sensor acquisition learning state signal transport processor Treatment Analysis, when bad learning state occurs, processor triggers prompting module, it is characterized in that:
(1) processor method according to claims 1 to 9 to learning state data is added up, and calculates the statistic changed along with the change of learning state；
(2) bad learning state is set according to statistic and reminds threshold value；
(3) when learning state data are beyond reminding threshold range, and processor triggers prompting module and reminds.
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| CN107479709A (en) * | 2017-01-04 | 2017-12-15 | 深圳市心流科技有限公司 | System and method for carrying out neural feedback training using video-game |
| CN108887961A (en) * | 2018-06-20 | 2018-11-27 | 新华网股份有限公司 | Seat and focus evaluating method based on seat |
| 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 |
| CN110599748A (en) * | 2019-09-20 | 2019-12-20 | 英华达（上海）科技有限公司 | Reminding method and reminding device applying same |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107479709A (en) * | 2017-01-04 | 2017-12-15 | 深圳市心流科技有限公司 | System and method for carrying out neural feedback training using video-game |
| CN108887961A (en) * | 2018-06-20 | 2018-11-27 | 新华网股份有限公司 | Seat and focus evaluating method based on seat |
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| CN110599748A (en) * | 2019-09-20 | 2019-12-20 | 英华达（上海）科技有限公司 | Reminding method and reminding device applying same |
| 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 |
| TWI747108B (en) * | 2019-09-20 | 2021-11-21 | 英華達股份有限公司 | Reminder method and reminder device using the 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 |
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