CN108197082A - A kind of step-recording method according to paces Reliability estimation - Google Patents
A kind of step-recording method according to paces Reliability estimation Download PDFInfo
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Abstract
The present invention discloses a kind of step-recording method according to paces Reliability estimation, makees paces Reliability estimation twice to vertical acceleration and forward acceleration data；When estimating for the first time, the approximate test for calculating two number of axle evidences is poor, and a part of static or minor motion gait is filtered out according to the size of approximate test difference；Second when estimating, calculates the high threshold and Low threshold in time window, determines that there are the time windows of credible paces according to the two difference；Vertical acceleration data point is traversed in the time window for meeting condition, the time point of paces variation is detected according to its comparison with Low threshold, middle threshold value；The method of the present invention precision is high, and meter step effect is good, real-time.
Description
Technical field
The step-recording method of acceleration is the present invention relates to the use of, is walked more particularly to a kind of according to the meter of paces Reliability estimation
Method.
Background technology
During pedestrian's walking, cyclically-varying can be showed by being fixed on the three axis accelerometer of waist, particularly vertical acceleration
Degree and forward acceleration.By the processing to acceleration information, the paces of pedestrian can be detected.It will usually hang down in conventional method
It is this there are multiple peak-to-valley values in a cycle to the threshold value of acceleration comparison setting, but due to factors such as noise and errors
Method be easy to cause the problem of more meter steps, and meter step accuracy is relatively low.In addition, usually using the time of regular length in conventional method
Window if beginning or end position, algorithm of the generation in time window can not detect this paces to paces just, has leakage meter step
The problem of.
Invention content
Technical problem：The present invention provide it is a kind of effectively, conveniently, accurately, real-time resolving speed it is fast, improve accuracy and effect
The step-recording method according to paces Reliability estimation of rate.
Technical solution：The step-recording method according to paces Reliability estimation of the present invention, the hardware device implemented include, add
Speedometer, control solving unit, power module, host computer；The accelerometer passes through iic bus phase with control solving unit
Even；The power module is connected with control solving unit；The host computer passes through serial bus phase with control solving unit
Even；The control solving unit is responsible for receiving acceleration information union；The host computer is responsible for showing real-time results.
The step-recording method according to paces Reliability estimation of the present invention, includes the following steps：
1) the accelerometer detection vertical acceleration and forward acceleration that human body is worn, the vertical acceleration detected is put
Enter customized buffering array Z, obtain Z time windows, the forward acceleration detected is put into customized buffering array Y, is obtained
To Y time windows, the length of two time windows is all N；
2) the approximate test difference stz of vertical acceleration is calculated according to the following formula：
Wherein, acc_z1It is first data in Z time windows, acc_ziIt is i-th of data in Z time windows, i is the Z times
The serial number of data in window, 1≤i≤N；
The approximate test difference sty of forward acceleration is calculated according to the following formula：
Wherein, acc_y1It is first data in Y time windows, acc_yjIt is j-th of data in Y time windows, j is the Y times
The serial number of data in window, 1≤j≤N；
3) judge the vertical estimation value p1z of first time paces confidence level and forward estimation value p1y respectively according to the following formula：
If 4) p1z and p1y is simultaneously 1, high threshold TrHz, Low threshold in Z time windows are calculated as follows
High threshold TrHy, Low threshold TrLy in TrLz and Y time windows return to step after otherwise updating vertical acceleration and forward acceleration
It is rapid 1)：
Wherein, vmax_zk1It is the maximum in Z time windows, m is the maximum number in Z time windows, vmin_zk2It is Z
Minimum in time window, n be Z time windows in minimum number, TrHzt-1And TrLzt-1It is in upper Z time windows respectively
High threshold and Low threshold, t are the number of current Z time windows, and k1 and k2 are respectively the sequence of maximum and minimum in Z time windows
Number, 1≤k1≤m, 1≤k2≤n, vmax_yk3It is the maximum in Y time windows, p is the maximum number in Y time windows,
vmin_yk4It is the minimum in Y time windows, q is the minimum number in Y time windows, TrHyt-1And TrLyt-1It is a upper Y respectively
High threshold and Low threshold in time window, t are the number of current Y time windows, k3 and k4 for be respectively in Y time windows maximum and
The serial number of minimum, 1≤k3≤p, 1≤k4≤q；
5) high-low threshold value calculated respectively according to the following formula in high-low threshold value difference vppz and the Y time window in Z time windows is poor
Value vppy：
Vppz=TrHz-TrLz
Vppy=TrHy-TrLy
Wherein, TrHz is the high threshold in Z time windows, and TrLz is the Low threshold in Z time windows, and TrHy is in Y time window
High threshold, TrLy be Y time windows in Low threshold；
6) judge the vertical estimation value p2z and forward estimation value p2y of second of paces confidence level respectively according to the following formula：
7) if p2z and p2y is simultaneously 1, illustrates to be likely to produce paces in Z time windows, calculate according to the following formula vertical
Otherwise the middle threshold value of acceleration updates return to step 1 after vertical acceleration and forward acceleration)：
8) by sequence of the data in Z time windows according to serial number from small to large, judged successively according to the following conditions：
acc_zi＜ TrLz ＜ acc_zi+1 (1)
acc_zi＞ TrLz ＞ acc_zi+1 (2)
Wherein, acc_ziIt is i-th of data, acc_z in Z time windowsi+1It is i+1 data in Z time windows, 1≤i≤
N-1 if a data first occur meets formula (1), judges that the data have passed through Low threshold from bottom to top, remembers data at this time
9) serial number s, enters step, if a data first occur meets formula (2), judge that the data have passed through low threshold from the top down
Value remembers that data sequence number at this time is w, updates vertical acceleration and obtain new Z time windows, return to step 2), if in Z time windows
All data be all both unsatisfactory for formula (1), be also unsatisfactory for formula (2), then returned after updating vertical acceleration and forward acceleration
Return step 1)；
Data point in the new Z time windows is：
Z={ acc_zw+1..., acc_zN..., acc_zN+w}
Wherein, acc_zw+1It is the w+1 data in old Z time windows, acc_zNIt is n-th number in old Z time windows
According to, hereafter it is the vertical acceleration newly received, acc_zN+wIt is w-th of the data newly received, new Z time window lengths are still
N；
9) by sequences of the s+1 in Z time windows to n-th data according to serial number from small to large, successively according to following
Condition is judged：
acc_zi＜ Tr ＜ acc_zi+1 (3)
acc_zi＞ TrLz ＞ acc_zi+1 (4)
Wherein, acc_ziIt is i-th of data, acc_z in Z time windowsi+1It is i+1 data, s+1≤i in Z time windows
≤ N-1 if a data occur meets formula (3), judges that the data have passed through middle threshold value from bottom to top, and count a step
It cuts down, if a data occur meets formula (4), judges that the data have passed through Low threshold from the top down, remember data sequence at this time
Number for r, update vertical acceleration simultaneously obtains new Z time windows, return to step 2), if s+1 in Z time windows is to n-th number
All data in are all unsatisfactory for formula (4), then update return to step 1 after vertical acceleration and forward acceleration), it is described new
Z time windows in data point be：
Z={ acc_zr+1..., acc_zN..., acc_zN+r}
Wherein, acc_zr+1It is the r+1 data in old Z time windows, acc_zNIt is n-th number in old Z time windows
According to, hereafter it is the vertical acceleration newly received, acc_zN+rIt is r-th of the data newly received, new Z time window lengths are still
N。
Further, in the method for the present invention, the vertical acceleration and forward acceleration of the step 1) should first do mean value filter
Wave.
Further, in the method for the present invention, the maximum wmax_z in Z time windows in the step 4)k1With Y time windows
Interior maximum vmax_yk3Determination method is determines in the following way respectively：
vmax_zk1-1＜ vmax_zk1And vmax_zk1+1＜ vmax_zk1
vmax_yk3-1＜ vmax_yk3And vmax_yk3+1＜ vmax_yk3
Wherein, vmax_zk1-1It is 1-1 data of kth, vmax_z in Z time windowsk1+1It is 1+1 number of kth in Z time windows
According to vmax_yk3-1It is 3-1 data of kth, vmax_y in Y time windowsk3+1It is 3+1 data of kth in Y time windows；
Minimum vmin_z in Z time windowsk2With the minimum vmin_y in Y time windowsk4Determination method for respectively according to
In the following manner determines：
vmin_zk2-1＞ vmin_zk2And vmin_zk2+1＞ vmin_zk
vmin_yk4-1＞ vmin_yk4And vmin_yk4+1＞ vmin_yk
Wherein, vmin_zk2-1It is 2-1 data of kth, vmin_z in Z time windowsk2+1It is 2+1 number of kth in Z time windows
According to vmin_yk4-1It is 4-1 data of kth, vmin_y in Y time windowsk4+1It is 4+1 data of kth in Y time windows.
Further, in the method for the present invention, the value range of the length N of buffering array Z and buffering array Y are 30 in step 1)
≤N≤70。
Further, in the method for the present invention, above-mentioned steps 1) in buffering array Z and buffer the length N=50 of array Y.
Advantageous effect：Compared with prior art, the present invention it has the following advantages：
(1) prior art generally only calculates vertical acceleration, and this method has considered vertical acceleration and forward direction accelerates
Degree enables the constraint at two number of axle strong points pedometer to filter out original place and the invalid paces such as shakes, marks time, turning, only record to
Move ahead into paces, meter step result is relatively reliable, and it is more accurate that applied to dead reckoning algorithm when can calculate position.
(2) conventional method calculates standard deviation and needs to traverse data twice, calculates mean value, second of calculating for the first time
Standard deviation, the approximate test difference that this method uses does not need to calculate mean value, but still is able to the degree of fluctuation of description data point, time
Complexity is relatively low, and the speed of algorithm is improved in real-time resolving.
(3) this method makes Reliability estimation twice with different statistical parameters respectively, and second of estimation calculation amount is bigger
Estimate in first time.Estimation for the first time can filter out the apparent situation that paces are not present, under the basis for meeting this condition just into
Second of estimation of row, avoids some unnecessary calculating；Second of estimation can filter out most of static and minor motion
Situation only has in the larger time window there may be paces at remaining and detects paces, improves the accuracy and efficiency of algorithm.
(4) when using the prior art, if being exactly in the starting and ending position of time window at the time of generating paces,
Algorithm can not detect, can cause leakage step situation.The time window that this method uses is dynamic, and dynamic time windows make generation paces
At the time of be more likely to fall at the middle part of time window, therefore count step result precision higher.
Description of the drawings
Fig. 1 is a kind of pedometer hardware device schematic diagram used in the method for the present invention
Fig. 2 is a kind of step-recording method flow chart according to paces Reliability estimation of the invention
Fig. 3 is the dynamic threshold and p2z of vertical acceleration
Fig. 4 is the dynamic threshold and p2y of forward acceleration
Fig. 5 is meter step effect diagram
Specific embodiment
The present invention is further described with reference to embodiment and Figure of description.
As shown in Figure 1, a kind of step-recording method according to paces Reliability estimation of the present invention, the hardware device packet implemented
It includes, accelerometer, control solving unit, power module, host computer；The accelerometer passes through IIC with control solving unit
Bus is connected；The power module is connected with control solving unit；The host computer passes through serial ports with control solving unit
Bus is connected；The control solving unit is responsible for receiving acceleration information union；It is real-time that the host computer is responsible for display
As a result.
A kind of step-recording method according to paces Reliability estimation in the present embodiment, includes the following steps：
1) the accelerometer detection vertical acceleration and forward acceleration that human body is worn, the vertical acceleration detected is passed through
Customized buffering array Z is put into after mean filter, obtains Z time windows, customized buffering array Y is put by what is detected,
Obtain Y time windows, the length of two time windows is all N, and the value range of N is 30≤N≤70, N=50 in the present embodiment.
In another preferred embodiment of the present invention, vertical acceleration and forward acceleration should first do mean filter and place into
Customized buffering array to filter out the king-sized harmful data point of fluctuation, makes periodical trend more obvious.
2) the approximate test difference stz of vertical acceleration is calculated according to the following formula：
Wherein, acc_z1It is first data in Z time windows, acc_ziIt is i-th of data in Z time windows, i is the Z times
The serial number of data in window, 1≤i≤N.
The approximate test difference sty of forward acceleration is calculated according to the following formula：
Wherein, acc_y1It is first data in Y time windows, acc_yjIt is j-th of data in Y time windows, j is the Y times
The serial number of data in window, 1≤j≤N.
3) judge the vertical estimation value p1z of first time paces confidence level and forward estimation value p1y respectively according to the following formula：
If 4) p1z and p1y is simultaneously 1, high threshold TrHz, Low threshold in Z time windows are calculated as follows
High threshold TrHy, Low threshold TrLy in TrLz and Y time windows return to step after otherwise updating vertical acceleration and forward acceleration
It is rapid 1)：
Wherein, vmax_zk1It is the maximum in Z time windows, m is the maximum number in Z time windows, vmin_zk2It is Z
Minimum in time window, n be Z time windows in minimum number, TrHzt-1And TrLzt-1It is in upper Z time windows respectively
High threshold and Low threshold, t are the number of current Z time windows, and k1 and k2 are respectively the sequence of maximum and minimum in Z time windows
Number, 1≤k1≤m, 1≤k2≤n, vmax_yk3It is the maximum in Y time windows, p is the maximum number in Y time windows,
vmin_yk4It is the minimum in Y time windows, q is the minimum number in Y time windows, TrHyt-1And TrLyt-1It is a upper Y respectively
High threshold and Low threshold in time window, t are the number of current Y time windows, k3 and k4 for be respectively in Y time windows maximum and
The serial number of minimum, 1≤k3≤p, 1≤k4≤q.
In another preferred embodiment of the present invention, the maximum vmax_z in Z time windowsk1With it is very big in Y time windows
Value vmax_yk3Determination method determines in the following way respectively：
vmax_zk1-1＜ vmax_zk1And vmax_zk1+1＜ vmax_zk1
vmax_yk3-1＜ vmax_yk3And vmax_yk3+1＜ vmax_yk3
Wherein, vmax_zk1-1It is 1-1 data of kth, vmax_z in Z time windowsk1+1It is 1+1 number of kth in Z time windows
According to vmax_yk3-1It is 3-1 data of kth, vmax_y in Y time windowsk3+1It is 3+1 data of kth in Y time windows；
Minimum vmin_z in Z time windowsk2With the minimum vmin_y in Y time windowsk4Determination method for respectively according to
In the following manner determines：
vmin_zk2-1＞ vmin_zk2And vmin_zk2+1＞ vmin_zk
vmin_yk4-1＞ vmin_yk4And vmin_yk4+1＞ vmin_yk
Wherein, vmin_zk2-1It is 2-1 data of kth, vmin_z in Z time windowsk2+1It is 2+1 number of kth in Z time windows
According to vmin_yk4-1It is 4-1 data of kth, vmin_y in Y time windowsk4+1It is 4+1 data of kth in Y time windows.
5) high-low threshold value calculated respectively according to the following formula in high-low threshold value difference vppz and the Y time window in Z time windows is poor
Value vppy：
Vppz=TrHz-TrLz
Vppy=TrHy-TrLy
Wherein, TrHz is the high threshold in Z time windows, and TrLz is the Low threshold in Z time windows, and TrHy is in Y time window
High threshold, TrLy be Y time windows in Low threshold.
6) judge the vertical estimation value p2z and forward estimation value p2y of second of paces confidence level respectively according to the following formula：
High threshold, Low threshold in Z time windows, middle threshold value and vertical estimation value p2z result of calculations are as shown in figure 3, during Y
Between high threshold in window, Low threshold, middle threshold value and forward estimation value p2y result of calculations it is as shown in Figure 4.
7) if p2z and p2y is simultaneously 1, illustrates to be likely to produce paces in Z time windows, calculate according to the following formula vertical
Otherwise the middle threshold value of acceleration updates return to step 1 after vertical acceleration and forward acceleration)：
8) by sequence of the data in Z time windows according to serial number from small to large, judged successively according to the following conditions：
acc_zi＜ TrLz ＜ acc_zi+1 (1)
acc_zi＞ TrLz ＞ acc_zi+1 (2)
Wherein, acc_ziIt is i-th of data, acc_z in Z time windowsi+1It is i+1 data in Z time windows, 1≤i≤
N-1 if a data first occur meets formula (1), judges that the data have passed through Low threshold from bottom to top, remembers data at this time
9) serial number s, enters step, if a data first occur meets formula (2), judge that the data have passed through low threshold from the top down
Value remembers that data sequence number at this time is w, updates vertical acceleration and obtain new Z time windows, return to step 2), if in Z time windows
All data be all both unsatisfactory for formula (1), be also unsatisfactory for formula (2), then returned after updating vertical acceleration and forward acceleration
Return step 1).
Data point in the new Z time windows is：
Z={ acc_zw+1..., acc_zN..., acc_zN+w}
Wherein, acc_zw+1It is the w+1 data in old Z time windows, acc_zNIt is n-th number in old Z time windows
According to, hereafter it is the vertical acceleration newly received, acc_zN+wIt is w-th of the data newly received, new Z time window lengths are still
N。
9) by sequences of the s+1 in Z time windows to n-th data according to serial number from small to large, successively according to following
Condition is judged：
acc_zi＜ Tr ＜ acc_zi+1 (3)
acc_zi＞ TrLz ＞ acc_zi+1 (4)
Wherein, acc_ziIt is i-th of data, acc_z in Z time windowsi+1It is i+1 data, s+1≤i in Z time windows
≤ N-1 if a data occur meets formula (3), judges that the data have passed through middle threshold value from bottom to top, and count a step
It cuts down, if a data occur meets formula (4), judges that the data have passed through Low threshold from the top down, remember data sequence at this time
Number for r, update vertical acceleration simultaneously obtains new Z time windows, return to step 2), if s+1 in Z time windows is to n-th number
All data in are all unsatisfactory for formula (4), then update return to step 1 after vertical acceleration and forward acceleration), it is described new
Z time windows in data point be：
Z={ acc_zr+1..., acc_zN..., acc_zN+r}
Wherein, acc_zr+1It is the r+1 data in old Z time windows, acc_zNIt is n-th number in old Z time windows
According to, hereafter it is the vertical acceleration newly received, acc_zN+rIt is r-th of the data newly received, new Z time window lengths are still
N。
The specific workflow figure of the present embodiment is as shown in Fig. 2, meter step effect is as shown in Figure 5.
Above-described embodiment is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill of the art
For personnel, without departing from the principle of the present invention, several improvement and equivalent replacement can also be made, these are to the present invention
Claim be improved with the technical solution after equivalent replacement, each fall within protection scope of the present invention.
Claims (5)
1. a kind of step-recording method according to paces Reliability estimation, which is characterized in that this method includes the following steps：
1) the accelerometer detection vertical acceleration and forward acceleration that human body is worn, the vertical acceleration detected is put into certainly
The buffering array Z of definition, obtains Z time windows, the forward acceleration detected is put into customized buffering array Y, when obtaining Y
Between window, the length of two time windows is all N；
2) the approximate test difference stz of vertical acceleration is calculated according to the following formula：
Wherein, acc_z1It is first data in Z time windows, acc_ziIt is i-th of data in Z time windows, i is in Z time windows
The serial number of data, 1≤i≤N；
The approximate test difference sty of forward acceleration is calculated according to the following formula：
Wherein, acc_y1It is first data in Y time windows, acc_yjIt is j-th of data in Y time windows, j is in Y time windows
The serial number of data, 1≤j≤N；
3) judge the vertical estimation value p1z of first time paces confidence level and forward estimation value p1y respectively according to the following formula：
If 4) p1z and p1y is simultaneously 1, high threshold TrHz, Low threshold TrLz and Y in Z time windows are calculated as follows
Otherwise high threshold TrHy, Low threshold TrLy in time window update return to step 1 after vertical acceleration and forward acceleration)：
Wherein, vmax_zk1It is the maximum in Z time windows, m is the maximum number in Z time windows, vmin_zk2It is Z time windows
Interior minimum, n be Z time windows in minimum number, TrHzt-1And TrLzt-1It is the high threshold in upper Z time windows respectively
And Low threshold, t are the number of current Z time windows, k1 and k2 are respectively the serial number of maximum and minimum in Z time windows, 1≤k1
≤ m, 1≤k2≤n, vmax_yk3It is the maximum in Y time windows, p is the maximum number in Y time windows, vmin_yk4It is Y
Minimum in time window, q be Y time windows in minimum number, TrHyt-1And TrLyt-1It is in upper Y time windows respectively
High threshold and Low threshold, t are the number of current Y time windows, and it is respectively the sequence of maximum and minimum in Y time windows that k3 and k4, which are,
Number, 1≤k3≤p, 1≤k4≤q；
5) the high-low threshold value difference in high-low threshold value difference vppz and the Y time window in Z time windows is calculated respectively according to the following formula
vppy：
Vppz=TrHz--TrLz
Vppy=TrHy--TrLy
Wherein, TrHz is the high threshold in Z time windows, and TrLz is the Low threshold in Z time windows, and TrHy is the height in Y time windows
Threshold value, TrLy are the Low threshold in Y time windows；
6) judge the vertical estimation value p2z and forward estimation value p2y of second of paces confidence level respectively according to the following formula：
7) if p2z and p2y is simultaneously 1, illustrates to be likely to produce paces in Z time windows, calculate vertical acceleration according to the following formula
Otherwise the middle threshold value of degree updates return to step 1 after vertical acceleration and forward acceleration)：
8) by sequence of the data in Z time windows according to serial number from small to large, judged successively according to the following conditions：
acc_zi＜ TrLz ＜ acc_zi+1 (1)
acc_zi＞ TrLz ＞ acc_zi+1 (2)
Wherein, acc_ziIt is i-th of data, acc_z in Z time windowsi+1It is i+1 data in Z time windows, 1≤i≤N-1, if
First there are a data and meet formula (1), then judge that the data have passed through Low threshold from bottom to top, remember that data sequence number at this time is
9) s is entered step, if a data first occur meets formula (2), judge that the data have passed through Low threshold from the top down, remember
Data sequence number at this time is w, updates vertical acceleration and obtains new Z time windows, return to step 2), if the institute in Z time windows
There are data to be all both unsatisfactory for formula (1), be also unsatisfactory for formula (2), then return to step after updating vertical acceleration and forward acceleration
It is rapid 1)；
Data point in the new Z time windows is：
Z={ acc_zw+1..., acc_zN..., acc_zN+w}
Wherein, acc_zw+1It is the w+1 data in old Z time windows, acc_zNIt is n-th data in old Z time windows, hereafter
It is the vertical acceleration newly received, acc_zN+wIt is w-th of the data newly received, new Z time window lengths are still N；
9) by sequences of the s+1 in Z time windows to n-th data according to serial number from small to large, successively according to the following conditions
Judged：
acc_zi＜ rr ＜ acc_zi+1 (3)
acc_zi＞ TrLz ＞ acc_zi+1 (4)
Wherein, acc_ziIt is i-th of data, acc_z in Z time windowsi+1It is i+1 data in Z time windows, s+1≤i≤N-1,
If a data occur meets formula (3), judge that the data have passed through middle threshold value from bottom to top, and count a paces, if going out
An existing data meet formula (4), then judge that the data have passed through Low threshold from the top down, remember that data sequence number at this time is r, more
New vertical acceleration simultaneously obtains new Z time windows, return to step 2), if the institute in s+1 to n-th data in Z time windows
There are data to be all unsatisfactory for formula (4), then update return to step 1 after vertical acceleration and forward acceleration), the new Z times
Data point in window is：
Z={ acc_zr+1..., acc_zN..., acc_zN+r}
Wherein, acc_zr+1It is the r+1 data in old Z time windows, acc_zNIt is n-th data in old Z time windows, hereafter
It is the vertical acceleration newly received, acc_zN+rIt is r-th of the data newly received, new Z time window lengths are still N.
A kind of 2. step-recording method according to paces Reliability estimation according to claim 1, which is characterized in that the step
1) vertical acceleration and forward acceleration should first do mean filter.
A kind of 3. step-recording method according to paces Reliability estimation according to claim 1, which is characterized in that the step
4) the maximum vmax_z in Z time windowsk1With the maximum vmax_y in Y time windowsk3Determination method is respectively according to following
Mode determines：
vmax_zk1-1＜ vmax_zk1And vmax_zk1+1＜ vmax_zk1
vmax_yk3-1＜ vmax_yk3And vmax_yk3+1＜ vmax_yk3
Wherein, vmax_zk1-1It is 1-1 data of kth, vmax_z in Z time windowsk1+1It is 1+1 data of kth in Z time windows,
vmax_yk3-1It is 3-1 data of kth, vmax_y in Y time windowsk3+1It is 3+1 data of kth in Y time windows；
Minimum vmin_z in Z time windowsk2With the minimum vmin_y in Y time windowsk4Determination method is respectively according to following
Mode determines：
vmin_zk2-1＞ vmin_zk2And vmin_zk2+1＞ vmin_zk
vmin_yk4-1＞ vmin_yk4And vmin_yk4+1＞ vmin_yk
Wherein, vmin_zk2-1It is 2-1 data of kth, vmin_z in Z time windowsk2+1It is 2+1 data of kth in Z time windows,
vmin_yk4-1It is 4-1 data of kth, vmin_y in Y time windowsk4+1It is 4+1 data of kth in Y time windows.
4. according to a kind of step-recording method according to paces Reliability estimation described in claim 1,2 or 3, which is characterized in that institute
It is 30≤N≤70 to state the length N value ranges of buffering array Z and buffering array Y in step 1).
A kind of 5. step-recording method according to paces Reliability estimation according to claim 4, which is characterized in that the step
1) the length N=50 of buffering array Z and buffering array Y in.
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