CN104990562B - Step-recording method based on auto-correlation computation - Google Patents
Step-recording method based on auto-correlation computation Download PDFInfo
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- CN104990562B CN104990562B CN201510378481.4A CN201510378481A CN104990562B CN 104990562 B CN104990562 B CN 104990562B CN 201510378481 A CN201510378481 A CN 201510378481A CN 104990562 B CN104990562 B CN 104990562B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
- G01C22/006—Pedometers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
- G01P15/18—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration in two or more dimensions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
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Abstract
The present invention proposes a kind of step-recording method based on auto-correlation computation, including：N number of 3-axis acceleration data in actual time window are gathered by the 3-axis acceleration sensor of pedometer, its modulus value is calculated and stores；Gather and calculate N number of 3-axis acceleration data modulus value in next time window, L modulus value is put into after above-mentioned N number of modulus value and the data after merging are designated as into sampled data x (n) before taking-up；Sampled data x (n) is filtered and its crest number is detected after auto-correlation computation；Using crest number as the step number detected in actual time window, it is added to obtain new current meter step sum with current meter step sum；Next time window of actual time window steps be repeated alternatively until that motion terminates as new actual time window.The present invention is highlighted the periodic characteristic of periodic signal in the signal comprising much noise by auto-correlation computation, then detects effective step number by counting the peak value of auto-correlation function, is effectively improved the accuracy of meter step.
Description
Technical field
The present invention relates to consumer application electric technology field, more particularly to one kind to be counted to user's step number
Step-recording method.
Background technology
With the fast development of wearable electronic equipment, pedometer is widely applied, and pedometer is a kind of daily
Exercise schedule monitor, the step number of people's walking, the distance that estimation people walk can be calculated, calculates and moves consumed Ka Lu
In, it is convenient for people to monitor body-building intensity, sports level and the metabolism of oneself at any time.
The used sensor of pedometer has polytype, such as Chinese patent literature《Walked using the meter of Magnetic Sensor
Device and measuring method》(CN101086450A) a kind of pedometer and measuring method using Magnetic Sensor is disclosed.This method exists
A bar magnet is added on one pin, reception device is installed on another pin, is by the horizontal component for detecting magnetic flux
No zero passage walks to count.This method exist in-convenience in use, it is affected by magnetic fields big the shortcomings that.Current most pedometers are to be based on
Microelectronics system MEMS 3-axis acceleration sensor gathers three number of axle evidences in user's motion process, by analyzing three axles
Acceleration information carries out counting step.Such as Chinese patent literature《Pedometer》(CN102297701A)、《One kind is in Android
The method that pedometer is realized on mobile phone》(CN104567912A) and《A kind of step-recording method based on 3 axle accelerometers and meter walk
Device》(CN103712632A) a kind of meter using threshold test is disclosed respectively walks algorithm.
《Pedometer》(CN102297701A) peak detected is judged by setting a variety of peak thresholds and time threshold
Whether value can indicate an effectively step.
《A kind of method that pedometer is realized in Android phone》(CN104567912A) threshold value of a crest is set
And the variance threshold values and window time of crest, calculate a time window in peak value variance whether variance threshold values with
Interior, whether the time interval for then detecting the peak value and previous peak value in time window and meets crest threshold condition to sentence
It is disconnected whether effectively to be walked using the crest as one.
《A kind of step-recording method and pedometer based on 3 axle accelerometers》(CN103712632A) acceleration of three axles is calculated first
The modulus value of degrees of data, then the difference of the maximum of acceleration modulus value and minimum value in preset window is calculated, and according to the difference
Size sets actual time window scope.It is in subtract successively to judge that acceleration modulus value whether there is continuous at least three modulus value successively
Small trend, if so, then whether first acceleration modulus value falls into the actual time window model with a upper paces initial time
In enclosing, if so, being then judged as an effectively step.
Above three patent of invention is all to judge whether currently processed peak value is one with time threshold and peak threshold
Individual effectively step, but problems with all be present：
The position difference that pedometer is placed when the 1st, using, the amplitude difference of user's motion and the two legs of user
Motion conditions are asymmetric, cause acceleration peak value caused by different users to be very different, so it is difficult to setting effectively
Peak threshold differentiate come whether the peak value to appearance represents an effectively step, cause meter step inaccurate.
2nd, in reality in use, because the motion conditions of user are extremely complex and equipment might not follow human body closely
Together move, the 3-axis acceleration data for causing to measure have very more noises, or even occur several in a paces
Peak valley, as shown in Fig. 2 because these peak Distributions are relatively scattered, its low-frequency component is overlapping with the low-frequency component that meter step needs, can not
Effectively removed by means of filtering, so inevitably resulting in the mistake of meter step.
The content of the invention
The invention aims to overcome the above-mentioned problems in the prior art.The present invention proposes a kind of be based on from phase
The step-recording method of computing is closed, this method is by the auto-correlation computation to sampled data, by week in the signal comprising much noise
The periodic characteristic of phase signal highlights, then detects effective step number by counting the peak value of auto-correlation function, effectively carries
The high accuracy of meter step.
The object of the present invention is achieved like this.The invention provides a kind of step-recording method based on auto-correlation computation, bag
Include following steps：
Step 1, whole meter step process is divided into the period of formed objects, the length of the time window of each period
For T, the 3-axis acceleration data in each time window, each time are gathered using the 3-axis acceleration sensor of pedometer
Sampling number in window is N number of, and it is 0 to initialize current meter step sum, and the sample frequency of the 3-axis acceleration sensor is
f；
Step 2, a time window is chosen as actual time window, is adopted by the 3-axis acceleration sensor of pedometer
Collect N number of 3-axis acceleration data in actual time window, and calculate N number of 3-axis acceleration data modulus value, by this N number of three
Axle acceleration data modulus value is stored；
Step 3, first with N number of three axle in next time window of the same method collection actual time window of step 2
Acceleration information simultaneously calculates N number of 3-axis acceleration data modulus value, and L 3-axis acceleration data modulus value is put before being then taken out
The data after N+L merging are obtained behind the N number of 3-axis acceleration data modulus value obtained in step 2 actual time window, and
Data after merging are designated as sampled data x (n), wherein, n is sampling sequence number, and n=1,2,3...N+L, N are each time
Sampling number in window, L are the 3-axis acceleration data modulus value taken out from next time window of actual time window
Number；
Step 4, sampled data x (n) is filtered by moving average filter method, its expression formula is：
Wherein, K is glide filter length factor；
Step 5, auto-correlation computation, auto-correlation function R are carried out to filtered sampled data x (n)x(m) expression formula is：
Wherein, m is the independent variable of auto-correlation function, and span is 0≤m≤N-1；
Step 6, auto-correlation function R is detectedx(m) crest number, using crest number as being detected in actual time window
Step number, and the step number is added with current meter step sum and walks sum as new current meter；
Step 7, using next time window of actual time window as new actual time window；
Step 8, repeat step 2 arrives step 7, and until motion terminates, stopping samples, and current meter step sum now is as whole
The meter step sum of individual motion process.
Preferably, the length T of the time window is 3-30 seconds, and sample frequency f is 10-40 hertz.
Preferably, the 3-axis acceleration data modulus value taken out in next time window from actual time window
Number L=Cf, wherein C are coefficient, and its value is 0.6-2.
Preferably, the value of the glide filter length factor K is 1-5.
Compared with background technology, the present invention has advantages below：
1st, peak threshold and time threshold need not be set, so avoiding due to peak threshold and time threshold
Unreasonable caused counting error is set.
2nd, by auto-correlation computation, high-frequency noise and low-frequency noise in signal are rejected completely, the cycle in signal
Composition displays completely.
3rd, the auto-correlation function after calculating is close to sinusoidal waveform.As shown in figure 3, so only need simply to count crest number
Step number is can obtain, greatly simplify and judge a logical design effectively walked.
Brief description of the drawings
Fig. 1 is the workflow schematic diagram of the step-recording method of the invention based on auto-correlation computation.
Fig. 2 is one group of pedometer 3-axis acceleration data modulus value of actual test.
Fig. 3 is to be obtained after carrying out auto-correlation computation to the modulus value of 3-axis acceleration data shown in Fig. 2 using matlab simulation softwares
The auto-correlation function curve arrived.
Embodiment
The method of the invention is applied to any pedometer equipment with 3-axis acceleration sensor, below only with wrist strap
It is introduced exemplified by formula pedometer.User presses meter step button, setting in motion after taking Wrist belt-type pedometer.The present invention according to
Following steps are carried out：
Step 1, the length T for taking time window is 10 seconds, and sample frequency f is 15 hertz, initializes current meter step sum
For 0, the 3-axis acceleration data in each time window are gathered using the 3-axis acceleration sensor of pedometer, each when
Between sampling number in window be N=10*15=150.
Step 2, first time window after meter step starts is chosen as actual time window, passes through three axles of pedometer
Acceleration transducer obtains 150 3-axis acceleration data in actual time window, and calculates these 3-axis acceleration data
Modulus value, this 150 3-axis acceleration data modulus value are stored.
Step 3, coefficient C=1.33, i.e. L=20 are taken, the next of actual time window is obtained with the same method of step 2
150 3-axis acceleration data in time window simultaneously calculate the modulus value of these 3-axis acceleration data, are then taken out
Preceding 20 3-axis accelerations data modulus value, and this 20 3-axis acceleration data modulus value are put into current time according to tandem
Behind the 150 3-axis acceleration data modulus value obtained in window, then the data after 170 merging are obtained, after merging
Data are designated as sampled data x (n), wherein, n is sampling sequence number, n=1,2,3...170.
Step 4, glide filter length factor K=2 is taken, then moving average filter length (2K+1) is equal to 5, passes through formulaMoving average filter is carried out to sampled data x (n).
Step 5：Formula is utilized to filtered sampled data x (n)Carry out certainly
Related operation, calculate auto-correlation function Rx(m), wherein independent variable m span is 0≤m≤149, for span
Each interior m can obtain a numerical value corresponding with m, and these numerical value are auto-correlation function Rx(m) at each m
Value.
Step 6：According to the orders of m from small to large, compare auto-correlation function R successivelyx(m) size of each numerical value, such as
Previous data and the latter data of some data of fruit are all less than that, then data are a crests herein, in this manner
All crests in the range of 0≤m≤149 are found out, using crest number as the step number detected in actual time window, and will
The step number is added with current meter step sum and walks sum as new current meter.
Step 7, it is next to start using next time window of actual time window as new actual time window
Secondary circulation.
Step 8, repeat step 2 arrives step 7, and until motion terminates, stopping samples, and current meter step sum now is as whole
The meter step sum of individual motion process.
Claims (3)
1. a kind of step-recording method based on auto-correlation computation, it is characterised in that comprise the following steps：
Step 1, whole meter step process is divided into the period of formed objects, the length of the time window of each period is T,
The 3-axis acceleration data in each time window, each time window are gathered using the 3-axis acceleration sensor of pedometer
Interior sampling number is N number of, and it is 0 to initialize current meter step sum, and the sample frequency of the 3-axis acceleration sensor is f；
Step 2, a time window is chosen as actual time window, is worked as by the 3-axis acceleration sensor collection of pedometer
N number of 3-axis acceleration data in preceding time window, and N number of 3-axis acceleration data modulus value is calculated, N number of three axle is added
Speed data modulus value is stored；
Step 3, first accelerated with N number of three axle in next time window of the same method collection actual time window of step 2
Degrees of data simultaneously calculates N number of 3-axis acceleration data modulus value, and L 3-axis acceleration data modulus value is put into step before being then taken out
The data after N+L merging are obtained behind the N number of 3-axis acceleration data modulus value obtained in rapid 2 actual time window, and will be closed
Data after and are designated as sampled data x (n), wherein, n is sampling sequence number, and n=1,2,3...N+L, N are each time window
Interior sampling number, L are for the 3-axis acceleration data modulus value taken out from next time window of actual time window
Number；
Step 4, sampled data x (n) is filtered by moving average filter method, its expression formula is：
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Wherein, K is glide filter length factor；
Step 5, auto-correlation computation, auto-correlation function R are carried out to filtered sampled data x (n)x(m) expression formula is：
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<msub>
<mi>R</mi>
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Wherein, m is the independent variable of auto-correlation function, and span is 0≤m≤N-1；
Step 6, auto-correlation function R is detectedx(m) crest number, using crest number as the step number detected in actual time window,
And the step number is added with current meter step sum and walks sum as new current meter；
Step 7, using next time window of actual time window as new actual time window；
Step 8, repeat step 2 arrives step 7, and until motion terminates, stopping samples, and current meter step sum now is entirely to transport
The meter step sum of dynamic process.
2. a kind of step-recording method based on auto-correlation computation according to claim 1, it is characterised in that when described in step 1
Between window length T be 3-30 seconds, sample frequency f is 10-40 hertz.
3. a kind of step-recording method based on auto-correlation computation according to claim 1, it is characterised in that sliding described in step 4
The value of dynamic filter length COEFFICIENT K is 1-5.
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| CN107462258B (en) * | 2017-07-13 | 2020-06-16 | 河海大学 | Step counting method based on mobile phone three-axis acceleration sensor |
| CN107515010A (en) * | 2017-08-28 | 2017-12-26 | 五邑大学 | The data processing method and pedometer device of a kind of pedometer |
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| CN108937852B (en) * | 2018-05-28 | 2021-06-22 | 深圳市北高智电子有限公司 | Intelligent step counting operation method |
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| CN111238527B (en) * | 2020-01-15 | 2023-09-29 | 桂林市优创电子科技有限公司 | Step counting method based on triaxial acceleration sensor |
| CN111536989B (en) * | 2020-05-26 | 2021-08-24 | 江南大学 | Adaptive time window step counting method based on peak detection |
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