CN104990562A - Step counting method based on autocorrecting computing - Google Patents

Step counting method based on autocorrecting computing Download PDF

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CN104990562A
CN104990562A CN201510378481.4A CN201510378481A CN104990562A CN 104990562 A CN104990562 A CN 104990562A CN 201510378481 A CN201510378481 A CN 201510378481A CN 104990562 A CN104990562 A CN 104990562A
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time window
axis acceleration
acceleration data
data
modulus value
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CN104990562B (en
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王建平
徐超
孙伟
李奇越
穆道明
朱程辉
徐晓冰
戴雷
秦剑
邓凡李
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Huangshan Development Investment Group Co.,Ltd.
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Hefei University of Technology
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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
    • 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
    • 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 invention provides a step counting method based on autocorrecting computing which comprises the following steps: collecting N three-axis acceleration data in a current time window by a three-axis acceleration sensor of a pedometer, calculating the module values of the N three-axis acceleration data and storing the calculated module values; collecting and calculating the module values of N three-axis acceleration data in a next time window, and taking the first L module values to be put behind the N module values to obtain N+L merged data, denoting the N+L merged data as sample data x (n); performing filtering and autocorrecting computing on the sample data x (n) to detect the number of wave crests of the sample data x (n); taking the number of wave crests as the step number detected in the current time window, and adding the step number and the current total step number together to obtain new current total step number; taking the next time window of the current time window as a new current time window and repeating the steps until the movement course is finished. According to the step counting method based on autocorrecting computing provided by the invention, through autocorrecting computing, the periodic features of periodic signals are highlighted in signals containing a great deal of noise, and the effective step number is detected according to the counted number of crest values of autocorrelation function, and the step counting accuracy is effectively improved.

Description

Based on the step-recording method of auto-correlation computation
Technical field
The present invention relates to consumer application electric technology field, particularly relate to a kind of step-recording method that can count user's step number.
Background technology
Along with the fast development of wearable electronic equipment, passometer is widely applied, passometer is a kind of daily exercise Schedule monitoring device, the step number of people's walking can be calculated, estimate the distance of people's walking, calculate the calorie that consumes of moving, be convenient for people to monitor at any time the body-building intensity of oneself, sports level and metabolism.
The sensor used of passometer has polytype, and such as Chinese patent literature " using the passometer of Magnetic Sensor and measuring method " (CN101086450A) discloses a kind of passometer and the measuring method that use Magnetic Sensor.The method is an additional bar magnet on a pin, and another pin installs receiving trap, by detecting the horizontal component whether zero passage meter step of magnetic flux.It is inconvenient to there is use in the method, large shortcoming affected by magnetic fields.Current most passometer is three number of axle certificates gathered based on the 3-axis acceleration sensor of microelectronics system MEMS in user's motion process, carries out counting step by analyzing 3-axis acceleration data.Such as Chinese patent literature " passometer " (CN102297701A), " a kind of method realizing passometer in Android phone " (CN104567912A) and " a kind of step-recording method based on 3 axle accelerometers and passometer " (CN103712632A) disclose a kind of meter step algorithm utilizing threshold test respectively.
By setting multiple peak threshold and time threshold, " passometer " (CN102297701A) judges whether the peak value detected can indicate an effectively step.
" a kind of method realizing passometer in Android phone " (CN104567912A) sets the threshold value of a crest and the variance threshold values of crest and widow time, whether the variance calculating the peak value in a time window is within variance threshold values, and whether time interval of then detecting this peak value and last peak value meets crest threshold condition to judge whether and this crest effectively can be walked as one in time window.
" a kind of step-recording method based on 3 axle accelerometers and passometer " (CN103712632A) first calculates the modulus value of 3-axis acceleration data, calculate the maximal value of acceleration modulus value and the difference of minimum value in preset window again, and actual time window scope is set according to described difference size.Judging whether acceleration modulus value exists continuous print at least three modulus value in reducing trend successively, and if so, then whether first acceleration modulus value and a upper paces initial time fall within the scope of described actual time window successively, being if so, then judged as an effectively step.
Above-mentioned three patents of invention are all judge whether the peak value when pre-treatment is an effectively step by time threshold and peak threshold, but all there is following problem:
The position that when 1, using, passometer is placed is different, the motion conditions of the amplitude difference of user's motion and the two legs of user is asymmetric, the acceleration peak value causing different users to produce is very different, so whether the effective peak threshold of very difficult setting represents an effective step to the peak value occurred differentiate, cause counting step inaccurate.
2, in the use of reality, motion conditions due to user is very complicated and equipment might not follow human body closely together moves, the 3-axis acceleration data recorded are caused to there is very many noises, even in paces, there are several peak valleys, as shown in Figure 2, because these peak Distribution are comparatively disperseed, it is overlapping that its low-frequency component and meter walk the low-frequency component needed, effectively cannot be removed by means of filtering, so the mistake counting step will inevitably be caused.
Summary of the invention
The object of the invention is to overcome the above-mentioned problems in the prior art.The present invention proposes a kind of step-recording method based on auto-correlation computation, the method is by the auto-correlation computation to sampled data, in the signal comprising much noise, the periodic characteristic of periodic signal is highlighted, peak value again by counting autocorrelation function detects effective step number, effectively raises the 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, comprise the following steps:
Step 1, whole meter step process is divided into the time period of formed objects, the length of the time window of each time period is T, the 3-axis acceleration sensor of employing passometer gathers the 3-axis acceleration data in each time window, sampling number in each time window is N number of, the current meter step of initialization adds up to 0, and the sample frequency of described 3-axis acceleration sensor is f;
Step 2, choose a time window as actual time window, gathered the N number of 3-axis acceleration data in actual time window by the 3-axis acceleration sensor of passometer, and calculate this N number of 3-axis acceleration data modulus value, this N number of 3-axis acceleration data modulus value is stored;
Step 3, first gather the N number of 3-axis acceleration data in the next time window of actual time window by the method that step 2 is same and calculate N number of 3-axis acceleration data modulus value, then before therefrom taking out, L 3-axis acceleration data modulus value is put into the data after obtaining N+L merging after N number of 3-axis acceleration data modulus value of obtaining in step 2 actual time window, and the data after merging are designated as sampled data x (n), wherein, n is sampling sequence number, n=1, 2, 3...N+L, N is the sampling number in each time window, L is the number of the 3-axis acceleration data modulus value of taking out from the next time window of actual time window,
Step 4, carries out filtering by moving average filter method to sampled data x (n), and its expression formula is:
x ( n ) = 1 2 K + 1 Σ k = - K K x ( n + k )
Wherein, K is glide filter length factor;
Step 5, carries out auto-correlation computation to filtered sampled data x (n), autocorrelation function R xm the expression formula of () is:
R x ( m ) = 1 N + L - m Σ n = 1 N + L x ( n ) x ( n + m )
Wherein, m is the independent variable of autocorrelation function, and span is 0≤m≤N-1;
Step 6, detects autocorrelation function R xm the crest number of (), using the step number that crest number detects in actual time window, and walks sum using this step number and is added with current counting and walks sum as new current counting;
Step 7, using the next time window of actual time window as new actual time window;
Step 8, repeats step 2 to step 7, until motion terminates, stops sampling, and current meter step sum is now the meter step sum of whole motion process.
Preferably, the length T of described time window is 3-30 second, and sample frequency f is 10-40 hertz.
Preferably, the number L=Cf of described 3-axis acceleration data modulus value of taking out in the next time window of actual time window, wherein C is coefficient, and its value is 0.6-2.
Preferably, the value of described glide filter length factor K is 1-5.
Compared with background technology, the present invention has the following advantages:
1, do not need to set peak threshold and time threshold, so avoid because peak threshold and time threshold arrange the unreasonable counting error caused.
2, by auto-correlation computation, the high frequency noise in signal and low-frequency noise are rejected completely, and the periodic component in signal displays completely.
3, the autocorrelation function after calculating is close to sinusoidal waveform.As shown in Figure 3, so only need simply to count crest number step number can be obtained, greatly simplify the logical design that judgement one effectively walks.
Accompanying drawing explanation
Fig. 1 is the workflow schematic diagram of the step-recording method that the present invention is based on auto-correlation computation.
Fig. 2 is one group of passometer 3-axis acceleration data modulus value of actual test.
The autocorrelation function curve of Fig. 3 for obtaining after utilizing matlab simulation software to carry out auto-correlation computation to the data of 3-axis acceleration shown in Fig. 2 modulus value.
Embodiment
The method of the invention is applicable to any pedometer equipment with 3-axis acceleration sensor, is only introduced for Wrist belt-type passometer below.User presses meter step button after bringing Wrist belt-type passometer, setting in motion.The present invention carries out according to following steps:
Step 1, the length T getting time window was 10 seconds, sample frequency f is 15 hertz, the current meter step of initialization adds up to 0, utilize the 3-axis acceleration sensor of passometer to gather the 3-axis acceleration data in each time window, the sampling number in each time window is N=10*15=150.
Step 2, choose meter and walk first time window after starting as actual time window, 150 3-axis acceleration data in actual time window are obtained by the 3-axis acceleration sensor of passometer, and calculate the modulus value of these 3-axis acceleration data, these 150 3-axis acceleration data modulus value are stored.
Step 3, get coefficient C=1.33, i.e. L=20, obtain 150 3-axis acceleration data in the next time window of actual time window by the method that step 2 is same and calculate the modulus value of these 3-axis acceleration data, then front 20 3-axis acceleration data modulus value are therefrom taken out, and these 20 3-axis acceleration data modulus value are put into after 150 3-axis acceleration data modulus value obtaining in actual time window according to tandem, then obtain the data after 170 merging altogether, data after merging are designated as sampled data x (n), wherein, n is sampling sequence number, n=1, 2, 3...170.
Step 4, gets glide filter length factor K=2, then moving average filter length (2K+1) equals 5, passes through formula moving average filter is carried out to sampled data x (n).
Step 5: formula is utilized to filtered sampled data x (n) carry out auto-correlation computation, calculate autocorrelation function R xm (), wherein the span of independent variable m is 0≤m≤149, and can obtain a numerical value corresponding with m for each m in span, these numerical value are autocorrelation function R xm () is in the value at each m place.
Step 6: according to m order from small to large, compare autocorrelation function R successively xthe size of each numerical value of (m), if the previous data of certain data and rear data all little than it, then data are crests herein, in this manner all crests in the scope of 0≤m≤149 are found out, using the step number that crest number detects in actual time window, and this step number is walked sum and is added with current counting and walks sum as new current counting.
Step 7, using the next time window of actual time window as new actual time window, to start to circulate next time.
Step 8, repeats step 2 to step 7, until motion terminates, stops sampling, and current meter step sum is now the meter step sum of whole motion process.

Claims (4)

1., based on a step-recording method for auto-correlation computation, it is characterized in that comprising the following steps:
Step 1, whole meter step process is divided into the time period of formed objects, the length of the time window of each time period is T, the 3-axis acceleration sensor of employing passometer gathers the 3-axis acceleration data in each time window, sampling number in each time window is N number of, the current meter step of initialization adds up to 0, and the sample frequency of described 3-axis acceleration sensor is f;
Step 2, choose a time window as actual time window, gathered the N number of 3-axis acceleration data in actual time window by the 3-axis acceleration sensor of passometer, and calculate this N number of 3-axis acceleration data modulus value, this N number of 3-axis acceleration data modulus value is stored;
Step 3, first gather the N number of 3-axis acceleration data in the next time window of actual time window by the method that step 2 is same and calculate N number of 3-axis acceleration data modulus value, then before therefrom taking out, L 3-axis acceleration data modulus value is put into the data after obtaining N+L merging after N number of 3-axis acceleration data modulus value of obtaining in step 2 actual time window, and the data after merging are designated as sampled data x (n), wherein, n is sampling sequence number, n=1, 2, 3...N+L, N is the sampling number in each time window, L is the number of the 3-axis acceleration data modulus value of taking out from the next time window of actual time window,
Step 4, carries out filtering by moving average filter method to sampled data x (n), and its expression formula is:
x ( n ) = 1 2 K + 1 Σ k = - K K x ( n + k )
Wherein, K is glide filter length factor;
Step 5, carries out auto-correlation computation to filtered sampled data x (n), autocorrelation function R xm the expression formula of () is:
R x ( m ) = 1 N + L - m Σ n = 1 N + L x ( n ) x ( n + m )
Wherein, m is the independent variable of autocorrelation function, and span is 0≤m≤N-1;
Step 6, detects autocorrelation function R xm the crest number of (), using the step number that crest number detects in actual time window, and walks sum using this step number and is added with current counting and walks sum as new current counting;
Step 7, using the next time window of actual time window as new actual time window;
Step 8, repeats step 2 to step 7, until motion terminates, stops sampling, and current meter step sum is now the meter step sum of whole motion process.
2. a kind of step-recording method based on auto-correlation computation according to claim 1, it is characterized in that, the length T of time window described in step 1 is 3-30 second, and 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 characterized in that, the number L=Cf of the 3-axis acceleration data modulus value of taking out in the next time window of actual time window described in step 3, wherein C is coefficient, and its value is 0.6-2.
4. a kind of step-recording method based on auto-correlation computation according to claim 1, it is characterized in that, the value of the length factor of glide filter described in step 4 K is 1-5.
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CN107462258A (en) * 2017-07-13 2017-12-12 河海大学 A kind of step-recording method based on mobile phone 3-axis acceleration sensor
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CN108896068A (en) * 2018-05-31 2018-11-27 康键信息技术（深圳）有限公司 Method, server, mobile terminal and the storage medium of step counting
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CN111536989A (en) * 2020-05-26 2020-08-14 江南大学 Adaptive time window step counting method based on peak detection
CN111880670A (en) * 2020-07-28 2020-11-03 深圳市恒达无限通信设备有限公司 Data processing method and system for intelligent wearable equipment in mobile phone
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CN106707721A (en) * 2015-11-12 2017-05-24 朗昇科技（苏州）有限公司 Multifunction watch
CN107462258B (en) * 2017-07-13 2020-06-16 河海大学 Step counting method based on mobile phone three-axis acceleration sensor
CN107462258A (en) * 2017-07-13 2017-12-12 河海大学 A kind of step-recording method based on mobile phone 3-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
WO2019061543A1 (en) * 2017-09-30 2019-04-04 华为技术有限公司 State determination method and portable device
CN110168547B (en) * 2017-09-30 2021-08-20 华为技术有限公司 State determination method and portable device
CN110168547A (en) * 2017-09-30 2019-08-23 华为技术有限公司 A kind of state determines method and portable equipment
CN108937852A (en) * 2018-05-28 2018-12-07 深圳市北高智电子有限公司 A kind of intelligence step counting, sleep monitor operation method
CN108896068A (en) * 2018-05-31 2018-11-27 康键信息技术（深圳）有限公司 Method, server, mobile terminal and the storage medium of step counting
CN108896068B (en) * 2018-05-31 2021-06-01 康键信息技术（深圳）有限公司 Step counting method, server, mobile terminal and storage medium
CN109124646A (en) * 2018-09-26 2019-01-04 北京壹氢科技有限公司 A kind of gait detection method suitable for wearing smart phone pedestrian
CN109124646B (en) * 2018-09-26 2021-06-18 北京壹氢科技有限公司 Gait detection method suitable for pedestrian wearing smart phone
CN109708631A (en) * 2018-12-26 2019-05-03 南京航空航天大学 A kind of pedestrian navigation navigational calibration method based on mobile phone modal idenlification
CN111238527A (en) * 2020-01-15 2020-06-05 桂林市优创电子科技有限公司 Step counting method based on three-axis acceleration sensor
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
CN111536989A (en) * 2020-05-26 2020-08-14 江南大学 Adaptive time window step counting method based on peak detection
CN111536989B (en) * 2020-05-26 2021-08-24 江南大学 Adaptive time window step counting method based on peak detection
CN111880670A (en) * 2020-07-28 2020-11-03 深圳市恒达无限通信设备有限公司 Data processing method and system for intelligent wearable equipment in mobile phone
CN111880670B (en) * 2020-07-28 2021-03-26 深圳市恒达无限通信设备有限公司 Data processing method and system for intelligent wearable equipment in mobile phone
CN113280832A (en) * 2021-07-13 2021-08-20 上海交通大学 Step counting method and system based on accelerometer

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