WO2015100707A1 - 计步方法及装置 - Google Patents

计步方法及装置 Download PDF

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Publication number
WO2015100707A1
WO2015100707A1 PCT/CN2014/001179 CN2014001179W WO2015100707A1 WO 2015100707 A1 WO2015100707 A1 WO 2015100707A1 CN 2014001179 W CN2014001179 W CN 2014001179W WO 2015100707 A1 WO2015100707 A1 WO 2015100707A1
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WO
WIPO (PCT)
Prior art keywords
acceleration signal
pass
signal
uniaxial
extreme point
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PCT/CN2014/001179
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English (en)
French (fr)
Inventor
刘崧
王福钋
李娜
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歌尔声学股份有限公司
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Application filed by 歌尔声学股份有限公司 filed Critical 歌尔声学股份有限公司
Priority to DK14876915.1T priority Critical patent/DK2985572T3/en
Priority to US14/891,581 priority patent/US10302449B2/en
Priority to JP2016520244A priority patent/JP6069590B2/ja
Priority to KR1020157031810A priority patent/KR101782240B1/ko
Priority to EP14876915.1A priority patent/EP2985572B1/en
Publication of WO2015100707A1 publication Critical patent/WO2015100707A1/zh

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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
    • 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
    • A61B5/1118—Determining activity level
    • 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
    • A61B5/112—Gait analysis
    • 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
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • 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
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/17—Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed

Definitions

  • the present invention relates to the field of sports equipment, and in particular to a step counting method and apparatus.
  • a pedometer is a device that can calculate the number of steps the wearer walks or runs (hereinafter referred to as walking). As people pay more attention to their health status, the pedometer has become an auxiliary tool for quantitative exercise programs and has been widely used.
  • pedometers are mainly divided into mechanical pedometers and electronic pedometers.
  • the mechanical pedometer uses the vibration of the pedometer internal reed or the elastic ball caused by the wearer to generate electronic pulses, and counts the number of these electronic pulses by the internal processor to realize the step counting function.
  • the mechanical pedometer has a lower cost, but its accuracy and sensitivity are poor.
  • An electronic pedometer is generally based on the output signal of the acceleration sensor to obtain the number of running walks of its wearer. Electronic pedometers have lower power consumption and better accuracy and sensitivity than mechanical pedometers. Therefore, electronic pedometers have become a hot spot in current pedometer research.
  • the process of human walking is a quasi-periodic process. Therefore, the acceleration generated by people in all directions during the running process, although varying in size, has the same quasi-periodicity, which is reflected in different directions. The same fundamental frequency is included in the acceleration.
  • the accelerometer-based pedometer can generate an oscillating acceleration signal during the wearer's walking and analyze the acceleration signal to obtain the wearer's running number. Specifically, the existing accelerometer-based pedometer determines the number of running runs of the wearer based on the number of peaks of the oscillating type acceleration signal generated thereby.
  • the disadvantage of the pedometer step counting method is that directly determining the running number by using the peak value of the oscillating type acceleration signal may result in poor step counting accuracy, which may affect the pedometer wearer's execution of its motion plan.
  • the present invention has been made to solve the above problems in the prior art, and an object thereof is to provide a step counting method and apparatus capable of more accurately counting the number of running of a pedometer wearer. .
  • a step counting method comprising the steps of repeatedly performing:
  • step g determining the number of running points obtained by the current step counting process according to the number of extreme points of the acceleration signal after removing the interference extreme point in the three uniaxial acceleration signals counted in step f), and calculating the running number The cumulative number of steps taken by the runner.
  • the fundamental frequency detection may use one or more of an autocorrelation function method, a cepstrum method, a linear predictive coding method, and an average amplitude difference function method.
  • performing baseband detection on each high-pass filtered uniaxial acceleration signal may include:
  • a(n) is the nth value of each high-pass filtered uniaxial acceleration signal
  • N is the predetermined length of the signal
  • ⁇ is the delay time
  • ⁇ ( ⁇ ) is the signal Normalized autocorrelation function
  • the method may further include: c1) using a filter that attenuates the signal energy from a low frequency to a high frequency, The signal is attenuated.
  • the removing the extreme point of the extreme value in the extreme value of the acceleration signal includes: filtering the interference extreme point in the extreme point of the acceleration signal by using the time interval; or filtering the extreme point of the acceleration signal by using the time interval and the amplitude The point of interference in the extreme.
  • the interference extreme point may comprise an acceleration signal extreme point, the time interval of the acceleration signal extreme point and its previous acceleration signal extreme point being less than a predetermined threshold.
  • the interference extreme point may include an amplitude non-maximum acceleration signal extreme point in the acceleration signal extreme point of each set of time intervals continuously less than a predetermined threshold.
  • the step g) may comprise:
  • the number of points of the acceleration signal after removing the interference extreme point corresponding to the uniaxial acceleration signal with the largest energy is determined according to the number of points of the acceleration signal obtained by the current step. Walking Step count.
  • the step counting method may further comprise: calculating a displacement from a second integral of time according to the at least one uniaxial acceleration signal.
  • a pedometer device comprising:
  • a uniaxial acceleration signal acquisition unit configured to acquire three uniaxial acceleration signals having a predetermined length from a three-axis output of the three-axis acceleration sensor worn by a walker;
  • a high-pass filtering unit configured to perform high-pass filtering on each of the single-axis acceleration signals acquired by the single-axis acceleration signal acquiring unit;
  • the base frequency detecting unit is configured to perform fundamental frequency detection on each high-pass filtered single-axis acceleration signal to obtain a fundamental frequency of each single-axis acceleration signal;
  • a low pass or band pass filter unit selects the lowest fundamental frequency of the three uniaxial acceleration signals as a cutoff frequency setting low pass or band pass filter and utilizes the low pass or band pass filter for each high pass filtered single
  • the axis acceleration signal is low pass or band pass filtered
  • An extreme point acquisition unit configured to obtain an acceleration signal extreme point in each low-pass or band-pass filtered single-axis acceleration signal and remove an interference extreme point therein;
  • a counting unit configured to count the number of extreme points of the acceleration signal after removing the interference extreme point in each low-pass or band-pass filtered single-axis acceleration signal
  • the step counting unit determines the running number obtained by the current step counting process according to the number of the extreme value points of the acceleration signal after removing the interference extreme point in the three single-axis acceleration signals counted by the counting unit, and calculates the running number The cumulative number of steps taken by the runner.
  • the base frequency detecting unit may include:
  • the attenuation filter is configured to attenuate each high-pass filtered single-axis acceleration signal by increasing the attenuation from low frequency to high frequency;
  • a calculation unit for determining an autocorrelation function ⁇ ( ⁇ ) of the signal output by the attenuation filter by the following formula:
  • N is the predetermined length of the signal
  • ⁇ is the delay time
  • ⁇ ( ⁇ ) is the normalized autocorrelation function of the signal
  • the fundamental frequency obtaining unit is configured to obtain a value of ⁇ corresponding to a maximum value of ⁇ ( ⁇ ), and output a reciprocal of the ⁇ value as a fundamental frequency of the high-pass filtered uniaxial acceleration signal.
  • the step counting unit may include an acceleration signal energy calculating unit configured to calculate energy of the respective uniaxial acceleration signals, and
  • the grading unit averages the number of extreme points of the acceleration signal after removing the interference extreme point corresponding to each axis, and uses the average as the current step The number of running runs obtained by the process; or, if the energy of each uniaxial acceleration signal differs greatly, the step counter unit removes the acceleration signal pole corresponding to the interference extreme point corresponding to the uniaxial acceleration signal with the largest energy The number of value points is used to determine the number of running runs obtained during the current round of the step counting process.
  • the step counting method and device of the present invention can better obtain the three single-axis acceleration signals by performing high-pass filtering, low-pass or band-pass filtering on the three single-axis acceleration signals output by the three-axis acceleration sensor.
  • the fundamental frequency component, and the removal of the interference extremum point on this basis can more accurately count the number of extreme points in the uniaxial acceleration signal that exactly correspond to the running number, so that the step can be accurately performed, which is beneficial to the calculation.
  • the wearer of the stepper accurately monitors the exercise plan.
  • FIG. 1 is a schematic view showing an example of an acceleration signal generated by a three-axis acceleration sensor in three directions during a wearer's running;
  • Figure 2 is a block diagram showing a step counting method according to an embodiment of the present invention.
  • Figure 3a is a signal diagram showing a representative normalized uniaxial acceleration signal having a predetermined length output from a triaxial acceleration sensor
  • Figure 3b is a signal diagram showing the uniaxial acceleration signal after high pass filtering
  • Figure 3c is a signal diagram showing the uniaxial acceleration signal after low pass or band pass filtering
  • Figure 3d is a signal diagram showing an example of extreme points of a uniaxial acceleration signal after low pass or band pass filtering
  • FIG. 4 is a schematic diagram of a spectrum of a uniaxial acceleration signal
  • Figure 5 shows an example of a frequency response curve of a filter that attenuates signal energy from low frequency to high frequency
  • Figure 6 is a signal diagram showing another example of extreme points of a uniaxial acceleration signal after low pass or band pass filtering
  • Figure 7 is a block diagram showing a pedometer device according to an embodiment of the present invention.
  • Figure 8 shows schematically a block diagram of a server for performing the method according to the invention
  • Fig. 9 schematically shows a storage unit for holding or carrying program code implementing the method according to the invention.
  • the step counting method of the present invention is applicable to the step counting of a pedometer having a three-axis acceleration sensor.
  • a pedometer having a three-axis acceleration sensor generates an oscillating type acceleration signal of a different amplitude in all directions during the wearer's running.
  • 1 is a schematic view showing an example of an acceleration signal generated by a three-axis acceleration sensor in three directions during a wearer's running, in which a x /g, a y /g, a z /g respectively It is a normalized acceleration signal generated by the triaxial acceleration sensor on the x-axis, the y-axis, and the z-axis, and g represents the gravitational acceleration. As shown in Fig.
  • the amplitudes of a x /g, a y /g, and a z /g are different, they all contain the same fundamental frequency, which represents the left and right feet of the pedometer wearer.
  • the reciprocal of the one-step exercise cycle is also included in a x /g, a y /g, a z /g, and the multiplication component corresponds to the reciprocal of the motion period of the left or right foot step.
  • the acceleration signal may also contain higher frequency components produced by other rhythms of the body.
  • the present invention provides a step counting method for accurately obtaining the extreme point corresponding to the fundamental frequency component in the acceleration signal by processing the acceleration signal output from the three-axis acceleration sensor, thereby accurately obtaining the running number.
  • FIG. 2 is a block diagram showing a step counting method according to an embodiment of the present invention. As shown in FIG. 2, the step counting method according to an embodiment of the present invention includes the following steps:
  • step S10 three uniaxial acceleration signals having a predetermined length are acquired from the three-axis output of the triaxial acceleration sensor worn by the runner.
  • Figure 3a is a signal diagram showing a representative normalized uniaxial acceleration signal a/g having a predetermined length output from a triaxial acceleration sensor, where a represents acceleration and g represents gravitational acceleration.
  • the predetermined length can be selected according to the actual situation. If the predetermined length is too long, it is not easy to obtain the running number in real time, and if the predetermined length is too short, the step counting accuracy may decrease. In the example of FIG. 3, the predetermined length is selected to be 3 seconds, but the present invention is not limited to this.
  • step S20 high-pass filtering is performed on each of the acquired single-axis acceleration signals. Since each uniaxial acceleration signal output from the three-axis acceleration sensor usually contains a direct current component, and the presence of the direct current component interferes with the analysis of each uniaxial acceleration signal, high-pass filtering is used to remove the uniaxial acceleration signal. DC component.
  • Figure 3b is a signal diagram showing the uniaxial acceleration signal after high pass filtering. As can be seen from Figure 3b, after high-pass filtering, the uniaxial acceleration signal contains only the AC component.
  • step S30 baseband detection is performed on each high-pass filtered uniaxial acceleration signal to obtain a fundamental frequency of each uniaxial acceleration signal.
  • a plurality of frequency components corresponding to different body rhythms such as a fundamental frequency component, a frequency doubling component, and other high frequency components may be included.
  • 4 is a schematic diagram of the spectrum of a uniaxial acceleration signal.
  • the fundamental frequency component is closely related to the running number, and it is more accurate to obtain the running number according to the fundamental frequency component.
  • a classical method such as an autocorrelation function method, a cepstrum method, a linear predictive coding method, and an average amplitude difference function method commonly used in speech signal pitch detection can be used.
  • an autocorrelation function method can be used.
  • the autocorrelation function ⁇ ( ⁇ ) of the signal is first determined by the following formula:
  • a(n) is the nth value of the signal
  • N is the predetermined length of the signal
  • ⁇ is the delay time
  • ⁇ ( ⁇ ) is the normalized autocorrelation function of the signal. Then, the value of ⁇ corresponding to the maximum value of ⁇ ( ⁇ ) is obtained, and the reciprocal of the ⁇ value is the fundamental frequency of the signal.
  • the uniaxial acceleration signal can be selectively attenuated to suppress the high in the uniaxial acceleration signal.
  • the frequency component thereby highlighting the fundamental frequency component in the uniaxial acceleration signal, and reducing the error of the obtained fundamental frequency.
  • the uniaxial acceleration signal can be attenuated using a filter that attenuates the signal energy from low frequency to high frequency.
  • Figure 5 shows an example of a frequency response curve of a filter that attenuates signal energy from low frequency to high frequency. After the uniaxial acceleration signal is attenuated by the filter, the low frequency components in the signal are less attenuated, and the high frequency components are more attenuated. Thus, when the fundamental frequency is obtained by reusing the autocorrelation function method for the uniaxial acceleration signal passing through the filter, the obtained fundamental frequency is relatively accurate.
  • step S40 the lowest fundamental frequency of the three uniaxial acceleration signals is selected as the cutoff frequency to set the low pass or band pass filter, and the high pass filtered single axis is utilized by the low pass or band pass filter.
  • the acceleration signal is low pass or band pass filtered. After low-pass or band-pass filtering, a smoother signal can be obtained, which facilitates accurate counting of the extreme points of the acceleration signal corresponding to the running number.
  • Figure 3c is a signal diagram showing the uniaxial acceleration signal after low pass or band pass filtering.
  • FIG. 3d is a signal diagram showing an example of extreme points of a uniaxial acceleration signal after low pass or band pass filtering, where the + sign indicates the extreme point (including the maximum point and Minimum point).
  • Figure 3d shows a more specific example in which noise interference in low-pass or band-pass filtered uniaxial acceleration signals is almost non-existent. In a more general case, after low-pass or band-pass filtering, there will still be noise interference in the uniaxial acceleration signal, which is manifested by the presence of interference extreme points.
  • Figure 6 is a signal diagram showing another example of extreme points of a uniaxial acceleration signal after low pass or band pass filtering.
  • there are interference extreme points in the low-pass or band-pass filtered uniaxial acceleration signal (as indicated by the arrows in Figure 6). These interference extreme points do not represent the periodic motion. Extreme points only result in more steps, and removing these interference extremes will make the number of steps in the statistics more accurate. It is therefore necessary to remove these interference extreme points in order to accurately obtain the extreme points corresponding to the number of running runs.
  • the number of running runs only corresponds to the number of extreme points in the uniaxial acceleration signal, and has little to do with the exact position of these extreme points, in other words, as long as the appropriate number of extreme points are removed to ensure the left leg and The motion cycle of each step of the right leg corresponds to a maximum point. Therefore, the method of removing the interference extreme point may not be unique.
  • the interference extreme point may comprise an acceleration signal extreme point, the time interval of the acceleration signal extreme point and its previous acceleration signal extreme point being less than a predetermined threshold, wherein the predetermined threshold is far Less than the period of the fundamental component of the uniaxial acceleration signal.
  • the predetermined threshold is far Less than the period of the fundamental component of the uniaxial acceleration signal.
  • the interference extremum point may comprise an amplitude signal non-maximum acceleration signal extrema point in the set of acceleration signal extremes that are continuously less than a predetermined threshold for each set of time intervals.
  • a predetermined threshold for each set of time intervals.
  • step S60 the number of extreme points of the acceleration signal after removing the interference extreme point in each of the low-pass or band-pass filtered uniaxial acceleration signals is counted.
  • step S70 the number of running points obtained by the current step counting process is determined according to the number of extreme points of the acceleration signal after removing the interference extreme point in the three uniaxial acceleration signals counted in step S60, and Calculate the cumulative number of steps taken by the runner.
  • the number of extreme points of the acceleration signal after removing the interference extreme point corresponding to each axis can be The average is used as the running number obtained by the current step counting process.
  • the energy of each uniaxial acceleration signal has a large difference (it can be determined by setting a predetermined threshold to determine whether the energy difference is large)
  • the astigmatism point corresponding to the uniaxial acceleration signal with the largest energy can be removed.
  • the number of extreme points of the acceleration signal is used to determine the number of running runs obtained during the current step counting process.
  • the displacement may be calculated from the second integral of the time according to the at least one uniaxial acceleration signal to provide a reference for the actual moving distance for the runner.
  • the magnitude of the displacement it is also possible to distinguish between in-situ movement or actual running.
  • the step counting method of the present invention has been described above with reference to Figs.
  • the step counting method described in the present invention may be implemented by software, implemented by hardware, or implemented by a combination of software and hardware.
  • FIG. 7 is a block diagram showing a pedometer device according to an embodiment of the present invention.
  • the pedometer device 1000 includes: a triaxial acceleration sensor 100, a uniaxial acceleration signal acquisition unit 200, a high pass filtering unit 300, a fundamental frequency detecting unit 400, a low pass or band pass filtering unit 500, and an extreme point.
  • the uniaxial acceleration signal acquisition unit 200 is configured to acquire three uniaxial acceleration signals having a predetermined length from a three-axis output of the triaxial acceleration sensor 100 worn by the runner.
  • the high pass filtering unit 300 is configured to perform high pass filtering on each of the uniaxial acceleration signals acquired by the uniaxial acceleration signal acquiring unit 200.
  • the baseband detecting unit 400 is configured to perform baseband detection on each high-pass filtered uniaxial acceleration signal to obtain a fundamental frequency of each uniaxial acceleration signal.
  • the low pass or band pass filtering unit 500 selects the lowest fundamental frequency of the three uniaxial acceleration signals as the cutoff frequency setting low pass or band pass filter, and uses the low pass or band pass filter to filter each high pass pass
  • the axis acceleration signal is low pass or band pass filtered.
  • the extreme point acquisition unit 600 is configured to obtain an acceleration signal extreme point in each low pass or band pass filtered uniaxial acceleration signal and remove the interference extreme point therein.
  • the counting unit 700 is configured to count the number of acceleration signal extreme points after removing the interference extreme point in each of the low-pass or band-pass filtered single-axis acceleration signals.
  • the pedometer unit 800 determines the number of running points obtained by the current step counting process according to the number of extreme points of the acceleration signal after removing the interference extreme point among the three uniaxial acceleration signals counted by the counting unit 700, and calculates the running number obtained by the current counting step process. The cumulative number of steps taken by the runner.
  • the fundamental frequency detecting unit 400 may include: an attenuation filter, configured to perform attenuation processing on each high-pass filtered uniaxial acceleration signal in a manner of increasing attenuation from a low frequency to a high frequency; and a calculation unit for The equation finds the autocorrelation function ⁇ ( ⁇ ) of the signal output by the attenuation filter:
  • the fundamental frequency obtaining unit is configured to obtain a value of ⁇ corresponding to a maximum value of ⁇ ( ⁇ ), and output a reciprocal of the ⁇ value as a fundamental frequency of the high-pass filtered uniaxial acceleration signal.
  • the step counting unit 800 may include an acceleration signal energy calculating unit for calculating energy of the respective uniaxial acceleration signals, and if the energy of each uniaxial acceleration signal is not much different, the pedometer unit 800 for each axis The number of extreme points of the acceleration signal after removing the interference extreme point is averaged, and the average is used as the running number obtained by the current counting step; or if the energy of each uniaxial acceleration signal is different
  • the pedometer unit 800 determines the number of running runs obtained by the current step counting process according to the number of extreme points of the acceleration signal after removing the interference extreme point corresponding to the uniaxial acceleration signal with the largest energy.
  • the various component embodiments of the present invention may be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof.
  • a microprocessor or digital signal processor may be used in practice to implement some or all of the functionality of some or all of the components in accordance with embodiments of the present invention.
  • the invention may also be embodied as a device or device for performing some or all of the methods described herein.
  • Programs eg, computer programs and computer program products.
  • Such a program implementing the invention may be stored on a computer readable medium or may be in the form of one or more signals. Such signals may be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
  • FIG. 8 shows a server, such as an application server, that can implement the human motion state monitoring method according to the present invention.
  • the server conventionally includes a processor 110 and a computer program product or computer readable medium in the form of a memory 120.
  • the memory 120 may be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), an EPROM, a hard disk, or a ROM.
  • the memory 120 has a memory space 130 for program code 131 for performing any of the method steps described above.
  • storage space 130 for program code may include various program code 131 for implementing various steps in the above methods, respectively.
  • the program code can be read from or written to one or more computer program products.
  • These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks.
  • Such computer program products are typically portable or fixed storage units as shown in FIG.
  • the storage unit may have a storage section, a storage space, and the like arranged similarly to the storage 120 in the server of FIG.
  • the program code can be compressed, for example, in an appropriate form.
  • the storage unit comprises computer readable code 131' for performing the steps of the method according to the invention, ie code that can be read by a processor, such as 110, which, when run by the server, causes the server to execute Each of the steps in the method described above.

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Abstract

一种计步方法和装置，所述方法包括重复执行下述步骤：a）从走跑者佩戴的三轴加速度传感器的三轴输出中获取三个具有预定长度的单轴加速度信号（S10）；b）对每个单轴加速度信号进行高通滤波（S20）；c）对每个高通滤波后的单轴加速度信号进行基频检测，以获得基频（S30）；d）选择三个单轴加速度信号中最低的基频作截止频率设置低通或带通滤波器，并利用其对相应的高通滤波后的单轴加速度信号进行低通或带通滤波（S40）；e）在每个低通或带通滤波后的单轴加速度信号中获得加速度信号极值点并去除其中的干扰极值点（S50）；f）对去除干扰极值点后的加速度信号极值点的数目进行统计（S60）；g）确定所述走跑者走跑的累计步数（S70）。该方法能够精确地进行计步。

Description

计步方法及装置 技术领域
本发明涉及运动器械领域，具体地说，涉及一种计步方法及装置。
发明背景
计步器是一种可以计算其佩戴者行走或跑步(以下简称走跑)步数的装置。随着人们对健康状况的深入关注，计步器成为定量制定运动方案的辅助工具，并得到了广泛的使用。
目前，计步器主要分为机械式计步器和电子式计步器两种。机械式计步器利用其佩戴者走跑时所引起的计步器内部簧片或者弹力小球的振动来产生电子脉冲，并通过内部处理器统计这些电子脉冲的数目，从而实现计步功能。机械式计步器的成本比较低，但它的准确度和灵敏度较差。电子式计步器一般是基于加速度传感器的输出信号来获得其佩戴者的走跑步数。电子式计步器的功耗低，并且其精确度和灵敏度都优于机械式计步器，因此，电子式计步器成为目前计步器研究中的一个热点。
人的走跑过程是一个具有准周期性的过程，因此，人在走跑过程中在各方向上所产生的加速度尽管大小不一，但都具有同样的准周期性，这体现在不同方向的加速度中包含同样的基频。基于加速度传感器的计步器可以在其佩戴者的走跑过程中产生振荡型加速度信号并对该加速度信号进行分析，从而获得其佩戴者的走跑步数。具体说，现有的基于加速度传感器的计步器根据其所产生的振荡型加速度信号的峰值的数目来确定其佩戴者的走跑步数。这些计步器的计步方法的不足之处在于，直接利用振荡型加速度信号的峰值来确定走跑步数会导致计步精度欠佳，从而会影响计步器佩戴者对其运动方案的执行。
发明内容
本发明就是为了解决上述现有技术中存在的问题而做出的，其目的在于提供一种计步方法及装置，该计步方法及装置能够更加精确地统计计步器佩戴者的走跑步数。
为了实现上述目的，在本发明的一个方面，提供一种计步方法，该方法包括重复执行的下述步骤：
a)从走跑者佩戴的三轴加速度传感器的三轴输出中获取三个具有预定长度的单轴加速度信号；
b)对所获取的每个单轴加速度信号进行高通滤波；
c)对每个高通滤波后的单轴加速度信号进行基频检测，获得每个单轴加速度信号的基频；
d)选择三个单轴加速度信号中最低的基频作为截止频率设置低通或带通滤波器，并利用该低通或带通滤波器对每个高通滤波后的单轴加速度信号进行低通或带通滤波；
e)在每个低通或带通滤波后的单轴加速度信号中获得加速度信号极值点并去除其中的干扰极值点；
f)对每个低通或带通滤波后的单轴加速度信号中的去除干扰极值点后的加速度信号极值点的数目进行统计；
g)根据步骤f)所统计出的三个单轴加速度信号中去除干扰极值点后的加速度信号极值点的数目，确定本轮计步过程所获得的走跑步数，并计算所述走跑者走跑的累计步数。
所述基频检测可以使用自相关函数方法、倒谱方法、线性预测编码方法、平均幅度差函数方法中的一种或多种方法。优选地，对每个高通滤波后的单轴加速度信号进行基频检测可以包括：
c2)由下述公式求出每个高通滤波后的单轴加速度信号的自相关函数ρ(τ)：
Figure PCTCN2014001179-appb-000001
其中，a(n)为每个高通滤波后的单轴加速度信号的第n个值，N为该信号的预定长度，且0≤n＜N，τ为延迟时间，ρ(τ)为该信号的归一化自相关函数；
c3)求出ρ(τ)的最大值所对应的τ的值，并且该τ值的倒数即为该信号的基频。
进一步优选地，在求出每个高通滤波后的单轴加速度信号的自相关函数ρ(τ)之前还可以包括：c1)利用对信号能量的衰减从低频到高频递增的滤波器，对该信号进行衰减处理。
其中，所述去除加速度信号极值点中的干扰极值点包括：通过时间间隔滤除加速度信号极值点中的干扰极值点；或者，通过时间间隔和幅值滤除加速度信号极值点中的干扰极值点。
优选地，所述干扰极值点可以包括这样的加速度信号极值点，该加速度信号极值点与其前一个加速度信号极值点的时间间隔小于预定阈值。或者，所述干扰极值点可以包括每组时间间隔连续小于预定阈值的加速度信号极值点中的幅值非最大的加速度信号极值点。
优选地，所述步骤g)可以包括：
如果各个单轴加速度信号的能量相差不大，则对各轴所对应的去除干扰极值点后的加速度信号极值点的数目进行平均，以该平均数作为本轮计步过程所获得的走跑步数；
或者，如果各个单轴加速度信号的能量相差较大，则根据其中能量最大的单轴加速度信号所对应的去除干扰极值点后的加速度信号极值点的数目来确定本轮计步过程所获得的走跑 步数。
优选地，所述的计步方法还可以包括：根据至少一个单轴加速度信号对时间的二次积分计算出位移。
根据本发明的另一方面，提供一种计步装置，该装置包括：
三轴加速度传感器；
单轴加速度信号获取单元，用于从走跑者佩戴的所述三轴加速度传感器的三轴输出中获取三个具有预定长度的单轴加速度信号；
高通滤波单元，用于对单轴加速度信号获取单元所获取的每个单轴加速度信号进行高通滤波；
基频检测单元，用于对每个高通滤波后的单轴加速度信号进行基频检测，获得每个单轴加速度信号的基频；
低通或带通滤波单元，选择三个单轴加速度信号中最低的基频作为截止频率设置低通或带通滤波器，并利用该低通或带通滤波器对每个高通滤波后的单轴加速度信号进行低通或带通滤波；
极值点获取单元，用于在每个低通或带通滤波后的单轴加速度信号中获得加速度信号极值点并去除其中的干扰极值点；
计数单元，用于对每个低通或带通滤波后的单轴加速度信号中的去除干扰极值点后的加速度信号极值点的数目进行统计；
计步单元，根据计数单元所统计出的三个单轴加速度信号中去除干扰极值点后的加速度信号极值点的数目，确定本轮计步过程所获得的走跑步数，并计算所述走跑者走跑的累计步数。
优选地，所述基频检测单元可以包括：
衰减滤波器，用于对每个高通滤波后的单轴加速度信号按从低频到高频衰减程度递增的方式进行衰减处理；
计算单元，用于由下述公式求出所述衰减滤波器输出的信号的自相关函数ρ(τ)：
Figure PCTCN2014001179-appb-000002
其中，a(n)为该信号的第n个值，N为该信号的预定长度，且0≤n＜N，τ为延迟时间，ρ(τ)为该信号的归一化自相关函数；
基频获得单元，用于求出ρ(τ)的最大值所对应的τ的值，并且输出该τ值的倒数作为所述高通滤波后的单轴加速度信号的基频。
优选地，所述计步单元可以包括加速度信号能量计算单元，用于计算所述各个单轴加速度信号的能量，并且，
如果各个单轴加速度信号的能量相差不大，则所述计步单元对各轴所对应的去除干扰极值点后的加速度信号极值点的数目进行平均，以该平均数作为本轮计步过程所获得的走跑步数；或者，如果各个单轴加速度信号的能量相差较大，则所述计步单元根据其中能量最大的单轴加速度信号所对应的去除干扰极值点后的加速度信号极值点的数目来确定本轮计步过程所获得的走跑步数。
根据上面的说明可知，本发明的计步方法及装置通过对三轴加速度传感器输出的三个单轴加速度信号进行高通滤波、低通或带通滤波可以更好地获得这三个单轴加速度信号的基频分量，并在此基础所通过干扰极值点去除可以更精确地统计出单轴加速度信号中与走跑步数精确对应的极值点数目，从而可以精确地进行计步，有利于计步器的佩戴者对运动方案进行精确监控。
上述说明仅是本发明技术方案的概述，为了能够更清楚了解本发明的技术手段，而可依照说明书的内容予以实施，并且为了让本发明的上述和其它目的、特征和优点能够更明显易懂，以下特举本发明的具体实施方式。
附图简要说明
通过阅读下文优选实施方式的详细描述，各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。附图仅用于示出优选实施方式的目的，而并不认为是对本发明的限制。在附图中：
图1是示意图，示出了三轴加速度传感器在其佩戴者的走跑过程中在三个方向上产生的加速度信号的示例；
图2是方框图，示出了本发明的一个实施例所述的计步方法；
图3a是信号图，示出了从三轴加速度传感器输出的具有预定长度的有代表性的归一化单轴加速度信号；
图3b是信号图，示出了经过高通滤波后的单轴加速度信号；
图3c是信号图，示出了经过低通或带通滤波后的单轴加速度信号；
图3d是信号图，示出了经过低通或带通滤波后的单轴加速度信号的极值点的一个例子；
图4是单轴加速度信号的频谱示意图；
图5示出了对信号能量的衰减从低频到高频递增的滤波器的频率响应曲线的示例；
图6是信号图，示出了经过低通或带通滤波后的单轴加速度信号的极值点的另一个例子；
图7是方框图，示出了本发明的一个实施例所述的计步装置；
图8示意性地示出了用于执行根据本发明的方法的服务器的框图；以及
图9示意性地示出了用于保持或者携带实现根据本发明的方法的程序代码的存储单元。
具体实施方式
下面将结合附图和具体实施例对本发明进行详细的描述。
在下面的描述中，只通过说明的方式描述了本发明的某些示范性实施例。毋庸置疑，本领域的普通技术人员可以认识到，在不偏离本发明的精神和范围的情况下，可以用各种不同的方式对所述实施例进行修正。因此，附图和描述在本质上是说明性的，而不是用于限制权利要求的保护范围。在本说明书中，相同的附图标记表示相同或相似的部分。
本发明的计步方法适用于具有三轴加速度传感器的计步器的计步。具有三轴加速度传感器的计步器在其佩戴者的走跑过程中在各方向上会产生幅值不同的振荡型加速度信号。图1是示意图，示出了三轴加速度传感器在其佩戴者的走跑过程中在三个方向上产生的加速度信号的示例，其中，ax/g、ay/g、az/g分别是三轴加速度传感器在x轴、y轴和z轴上产生的归一化的加速度信号，g表示重力加速度。如图1所示，尽管ax/g、ay/g、az/g的幅值不同，但都包含同样的基频，该基频代表计步器佩戴者的左脚和右脚各迈一步的运动周期的倒数。另外，在ax/g、ay/g、az/g中还包含倍频分量，倍频分量对应着左脚或右脚单迈一步的运动周期的倒数。除此之外，加速度信号中还可能包含由身体的其它律动所产生的更高频率的分量。由于三轴加速度传感器的输出中除了基频分量外还包含高频分量以及其它噪声，因此，通过直接搜索加速度信号的极值点来确定走跑步数会导致计步不准。为此，本发明提供一种计步方法，通过对三轴加速度传感器输出的加速度信号进行处理来准确地获得加速度信号中的基频分量所对应的极值点，从而准确地获得走跑步数。
图2是方框图，示出了本发明的一个实施例所述的计步方法。如图2所示，本发明的一个实施例所述的计步方法包括如下步骤：
首先，在步骤S10中，从走跑者佩戴的三轴加速度传感器的三轴输出中获取三个具有预定长度的单轴加速度信号。图3a是信号图，示出了从三轴加速度传感器输出的具有预定长度的有代表性的归一化单轴加速度信号a/g，其中，a表示加速度，g表示重力加速度。预定长度可以根据实际情况进行选择，如果预定长度太长，则不易即时获得走跑步数，如果预定长度太短，则计步准确度可能会下降。在图3的例子中，预定长度选为3秒，但本发明不限于 此。
接着，在步骤S20中，对所获取的每个单轴加速度信号进行高通滤波。由于从三轴加速度传感器输出的各个单轴加速度信号通常会包含直流分量，而该直流分量的存在会对各个单轴加速度信号的分析产生干扰，因此，通过高通滤波来去除单轴加速度信号中的直流分量。图3b是信号图，示出了经过高通滤波后的单轴加速度信号。从图3b可以看到，经过高通滤波后，单轴加速度信号只包含交流分量。
之后，在步骤S30中，对每个高通滤波后的单轴加速度信号进行基频检测，获得每个单轴加速度信号的基频。如前面所述，在走跑过程所产生的单轴加速度信号中，可能会包含与不同的身体律动相对应的多种频率分量，如基频分量、倍频分量以及其它高频分量。图4是单轴加速度信号的频谱示意图。其中，基频分量与走跑步数关联紧密，而且根据基频分量获得走跑步数会更准确。为了能够获得只有基频分量的加速度信号，需要滤除加速度信号中的高频分量。而为了滤除高频分量，需要大致检测出基频分量的频率，以便构造合适的滤波器滤除基频分量之外的高频分量。
基频检测的方法很多，例如，可以使用语音信号基音检测中常用的自相关函数方法，倒谱方法，线性预测编码方法，平均幅度差函数方法等经典方法。优选地，可以使用自相关函数方法。
具体说，对于每个高通滤波后的单轴加速度信号，先由下述公式求出该信号的自相关函数ρ(τ)：
Figure PCTCN2014001179-appb-000003
其中，a(n)为该信号的第n个值，N为该信号的预定长度，且0≤n＜N，τ为延迟时间，ρ(τ)为该信号的归一化自相关函数。然后，求出ρ(τ)的最大值所对应的τ的值，该τ值的倒数即为该信号的基频。
在实际的单轴加速度信号中，基频之外的其它频率分量(例如倍频分量)有时会具有较大的能量，如图4所示。这样，在通过求ρ(τ)的最大值而获得该最大值所对应的τ值时，会产生较大的误差。因此，为了利用自相关函数方法准确地获得基频1/τ，在求自相关函数ρ(τ)之前，可以先对单轴加速度信号进行选择性衰减处理，以抑制单轴加速度信号中的高频分量，从而突出单轴加速度信号中的基频分量，减小所获得的基频的误差。在本发明的一个实施例中，可以利用对信号能量的衰减从低频到高频递增的滤波器来对单轴加速度信号进行衰减处 理。图5示出了对信号能量的衰减从低频到高频递增的滤波器的频率响应曲线的示例。单轴加速度信号通过该滤波器衰减后，信号中的低频分量得到较小的衰减，而高频分量则会得到较大的衰减。这样，对通过该滤波器后的单轴加速度信号再利用自相关函数方法求基频时，所获得的基频就比较准确。
然后，在步骤S40中，选择三个单轴加速度信号中最低的基频作为截止频率设置低通或带通滤波器，并利用该低通或带通滤波器对每个高通滤波后的单轴加速度信号进行低通或带通滤波。低通或带通滤波后，可以获得较为平滑的信号，从而便于准确统计与走跑步数对应的加速度信号的极值点。图3c是信号图，示出了经过低通或带通滤波后的单轴加速度信号。
接着，在步骤S50中，在每个低通或带通滤波后的单轴加速度信号中获得加速度信号极值点并去除加速度信号极值点中的干扰极值点。图3d是信号图，示出了经过低通或带通滤波后的单轴加速度信号的极值点的一个例子，其中，+号所表示的就是所述极值点(包括极大值点和极小值点)。图3d示出的是比较特殊的例子，其中，低通或带通滤波后的单轴加速度信号中的噪声干扰几乎不存在了。在更一般的情形中，低通或带通滤波后，单轴加速度信号中仍然会有噪声干扰存在，表现为会有干扰极值点的存在。图6是信号图，示出了经过低通或带通滤波后的单轴加速度信号的极值点的另一个例子。如图6所示，在低通或带通滤波后的单轴加速度信号中存在干扰极值点(如图6中的箭头所指示的)，这些干扰极值点不代表与周期性运动有关的极值点，仅会导致步数多计，去掉这些干扰极值点会使得统计的步数更准确。因此需要去除这些干扰极值点，以便准确地获得与走跑步数对应的极值点。
事实上，走跑步数只与单轴加速度信号中的极值点的数目对应，而与这些极值点的准确位置关系不大，换言之，只要去除合适数目的极值点，以保证左腿和右腿各迈一步的运动周期与一个极大值点对应即可。因此，干扰极值点的去除方法可以不唯一。
在本发明的一个实施例中，干扰极值点可以包括这样的加速度信号极值点，该加速度信号极值点与其前一个加速度信号极值点的时间间隔小于预定阈值，其中，该预定阈值远小于单轴加速度信号的基频分量的周期。在该实施例中，在每一组靠得较近的极值点中，只保留最左边的一个极值点，其余极值点则视为干扰极值点而去除。这种方式下，通过加速度信号极值点之间的时间间隔，滤除加速度信号极值点中的干扰极值点。
在本发明的另一个实施例中，干扰极值点可以包括每组时间间隔连续小于预定阈值的加速度信号极值点中的幅值非最大的加速度信号极值点。换言之，在该实施例中，在每一组靠得较近的极值点中，只保留幅值最大的加速度信号极值点，其余的极值点则视为干扰极值点而去除。这种方式下，通过加速度信号极值点之间的时间间隔和加速度信号极值点的幅值，滤除加速度信号极值点中的干扰极值点。
接着，在步骤S60中，对每个低通或带通滤波后的单轴加速度信号中的去除干扰极值点后的加速度信号极值点的数目进行统计。
然后，在步骤S70中，根据步骤S60所统计出的三个单轴加速度信号中去除干扰极值点后的加速度信号极值点的数目，确定本轮计步过程所获得的走跑步数，并计算所述走跑者走跑的累计步数。
例如，如果各个单轴加速度信号的能量相差不大(可以通过设置预定阈值来判断能量相差是否不大)，则可以对各轴所对应的去除干扰极值点后的加速度信号极值点的数目进行平均，以该平均数作为本轮计步过程所获得的走跑步数。又例如，如果各个单轴加速度信号的能量相差较大(可以通过设置预定阈值来判断能量相差是否较大)，则可以根据其中能量最大的单轴加速度信号所对应的去除干扰极值点后的加速度信号极值点的数目来确定本轮计步过程所获得的走跑步数。
重复上述步骤S10-S70，并将每一轮计步过程所获得的走跑步数累加起来，就可以得到总的累计步数。
另外，在上述方法中，还可以根据至少一个单轴加速度信号对时间的二次积分计算出位移，以便为走跑者提供实际运动距离的参考。另外，根据位移的大小还可以区分是原地运动还是实际的走跑。
如上参照图1-图6描述了本发明所述的计步方法。本发明所述的计步方法，可以采用软件实现，也可以采用硬件实现，或采用软件和硬件组合的方式实现。
图7是方框图，示出了本发明的一个实施例所述的计步装置。如图7所示，该计步装置1000包括：三轴加速度传感器100、单轴加速度信号获取单元200、高通滤波单元300、基频检测单元400、低通或带通滤波单元500、极值点获取单元600、计数单元700、计步单元800。
单轴加速度信号获取单元200用于从走跑者佩戴的三轴加速度传感器100的三轴输出中获取三个具有预定长度的单轴加速度信号。
高通滤波单元300用于对单轴加速度信号获取单元200所获取的每个单轴加速度信号进行高通滤波。
基频检测单元400用于对每个高通滤波后的单轴加速度信号进行基频检测，获得每个单轴加速度信号的基频。
低通或带通滤波单元500选择三个单轴加速度信号中最低的基频作为截止频率设置低通或带通滤波器，并利用该低通或带通滤波器对每个高通滤波后的单轴加速度信号进行低通或带通滤波。
极值点获取单元600用于在每个低通或带通滤波后的单轴加速度信号中获得加速度信号极值点并去除其中的干扰极值点。
计数单元700用于对每个低通或带通滤波后的单轴加速度信号中的去除干扰极值点后的加速度信号极值点的数目进行统计。
计步单元800根据计数单元700所统计出的三个单轴加速度信号中去除干扰极值点后的加速度信号极值点的数目，确定本轮计步过程所获得的走跑步数，并计算所述走跑者走跑的累计步数。
优选地，基频检测单元400可以包括：衰减滤波器，用于对每个高通滤波后的单轴加速度信号按从低频到高频衰减程度递增的方式进行衰减处理；计算单元，用于由下述公式求出所述衰减滤波器输出的信号的自相关函数ρ(τ)：
Figure PCTCN2014001179-appb-000004
其中，a(n)为该信号的第n个值，N为该信号的预定长度，且0≤n＜N，τ为延迟时间，ρ(τ)为该信号的归一化自相关函数；基频获得单元，用于求出ρ(τ)的最大值所对应的τ的值，并且输出该τ值的倒数作为所述高通滤波后的单轴加速度信号的基频。
优选地，计步单元800可以包括加速度信号能量计算单元，用于计算所述各个单轴加速度信号的能量，并且，如果各个单轴加速度信号的能量相差不大，则计步单元800对各轴所对应的去除干扰极值点后的加速度信号极值点的数目进行平均，以该平均数作为本轮计步过程所获得的走跑步数；或者，如果各个单轴加速度信号的能量相差较大，则计步单元800根据其中能量最大的单轴加速度信号所对应的去除干扰极值点后的加速度信号极值点的数目来确定本轮计步过程所获得的走跑步数。
如上参照附图以示例的方式描述了根据本发明所述的计步方法和装置。但是，本领域技术人员应当理解，对于上述本发明所提出的计步方法和装置，还可以在不脱离本发明内容的基础上做出各种改进。因此，本发明的保护范围应当由所附的权利要求书的内容确定。
需要说明的是：
本发明的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本发明实施例中的一些或者全部部件的一些或者全部功能。本发明还可以实现为用于执行这里所描述的方法的一部分或者全部的设备或者装置 程序(例如，计算机程序和计算机程序产品)。这样的实现本发明的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。
例如，图8示出了可以实现根据本发明的人体运动状态监视方法的服务器，例如应用服务器。该服务器传统上包括处理器110和以存储器120形式的计算机程序产品或者计算机可读介质。存储器120可以是诸如闪存、EEPROM(电可擦除可编程只读存储器)、EPROM、硬盘或者ROM之类的电子存储器。存储器120具有用于执行上述方法中的任何方法步骤的程序代码131的存储空间130。例如，用于程序代码的存储空间130可以包括分别用于实现上面的方法中的各种步骤的各个程序代码131。这些程序代码可以从一个或者多个计算机程序产品中读出或者写入到这一个或者多个计算机程序产品中。这些计算机程序产品包括诸如硬盘，紧致盘(CD)、存储卡或者软盘之类的程序代码载体。这样的计算机程序产品通常为如图9所示的便携式或者固定存储单元。该存储单元可以具有与图8的服务器中的存储器120类似布置的存储段、存储空间等。程序代码可以例如以适当形式进行压缩。通常，存储单元包括用于执行根据本发明的方法步骤的计算机可读代码131’，即可以由例如诸如110之类的处理器读取的代码，这些代码当由服务器运行时，导致该服务器执行上面所描述的方法中的各个步骤。
应该注意的是上述实施例对本发明进行说明而不是对本发明进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。单词“包括”不排除存在未列在权利要求中的元件或步骤。本发明可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。
在此处所提供的说明书中，说明了大量具体细节。然而，能够理解，本发明的实施例可以在没有这些具体细节的情况下被实践。在一些实例中，并未详细示出公知的方法、结构和技术，以便不模糊对本说明书的理解。本说明书中使用的语言主要是为了可读性和教导的目的而选择的，而不是为了解释或者限定本发明的主题而选择的。

Claims (13)

  1. 一种计步方法，其特征在于，该方法包括重复执行的下述步骤：
    从走跑者佩戴的三轴加速度传感器的三轴输出中获取三个具有预定长度的单轴加速度信号；
    对所获取的每个单轴加速度信号进行高通滤波；
    对每个高通滤波后的单轴加速度信号进行基频检测，获得每个单轴加速度信号的基频；
    选择三个单轴加速度信号中最低的基频作为截止频率设置低通或带通滤波器，并利用该低通或带通滤波器对每个高通滤波后的单轴加速度信号进行低通或带通滤波；
    在每个低通或带通滤波后的单轴加速度信号中获得加速度信号极值点并去除加速度信号极值点中的干扰极值点；
    对每个低通或带通滤波后的单轴加速度信号中的去除干扰极值点后的加速度信号极值点的数目进行统计；
    根据统计结果确定本轮计步过程所获得的走跑步数，并计算所述走跑者走跑的累计步数。
  2. 根据权利要求1所述的计步方法，其中，所述基频检测使用自相关函数方法、倒谱方法、线性预测编码方法、平均幅度差函数方法中的一种或多种方法。
  3. 根据权利要求2所述的计步方法，其中，所述对每个高通滤波后的单轴加速度信号进行基频检测包括：
    利用对信号能量的衰减从低频到高频递增的滤波器，对每个高通滤波后的单轴加速度信号进行衰减处理；
    由下述公式求出进行衰减处理后的每个高通滤波后的单轴加速度信号的自相关函数ρ(τ)：
    Figure PCTCN2014001179-appb-100001
    其中，a(n)为每个高通滤波后的单轴加速度信号的第n个值，N为该信号的预定长度，且0≤n＜N，τ为延迟时间，ρ(τ)为该信号的归一化自相关函数；
    求出ρ(τ)的最大值所对应的τ的值，并且该τ值的倒数即为该信号的基频。
  4. 如权利要求1所述的计步方法，其中，所述去除加速度信号极值点中的干扰极值点包括：通过时间间隔滤除加速度信号极值点中的干扰极值点；或者，通过时间间隔和幅值滤除加速度信号极值点中的干扰极值点。
  5. 如权利要求4所述的计步方法，其中，所述干扰极值点包括这样的加速度信号极值点，该加速度信号极值点与其前一个加速度信号极值点的时间间隔小于预定阈值。
  6. 如权利要求4所述的计步方法，其中，所述干扰极值点包括每组时间间隔连续小于预定阈值的加速度信号极值点中的幅值非最大的加速度信号极值点。
  7. 如权利要求1所述的计步方法，其中，所述根据统计结果确定本轮计步过程所获得的走跑步数包括：
    如果各个单轴加速度信号的能量相差不大，则对各轴所对应的去除干扰极值点后的加速度信号极值点的数目进行平均，以该平均数作为本轮计步过程所获得的走跑步数；
    或者，如果各个单轴加速度信号的能量相差较大，则根据其中能量最大的单轴加速度信号所对应的去除干扰极值点后的加速度信号极值点的数目来确定本轮计步过程所获得的走跑步数。
  8. 如权利要求1所述的计步方法，还包括：根据至少一个单轴加速度信号对时间的二次积分计算出位移。
  9. 一种计步装置，其特征在于，该装置包括：
    三轴加速度传感器(100)；
    单轴加速度信号获取单元(200)，用于从走跑者佩戴的所述三轴加速度传感器(100)的三轴输出中获取三个具有预定长度的单轴加速度信号；
    高通滤波单元(300)，用于对单轴加速度信号获取单元(200)所获取的每个单轴加速度信号进行高通滤波；
    基频检测单元(400)，用于对每个高通滤波后的单轴加速度信号进行基频检测，获得每个单轴加速度信号的基频；
    低通或带通滤波单元(500)，选择三个单轴加速度信号中最低的基频作为截止频率设置低通或带通滤波器，并利用该低通或带通滤波器对每个高通滤波后的单轴加速度信号进行低通或带通滤波；
    极值点获取单元(600)，用于在每个低通或带通滤波后的单轴加速度信号中获得加速度信号极值点并去除其中的干扰极值点；
    计数单元(700)，用于对每个低通或带通滤波后的单轴加速度信号中的去除干扰极值点后的加速度信号极值点的数目进行统计；
    计步单元(800)，根据计数单元(700)所统计结果确定本轮计步过程所获得的走跑步数，并计算所述走跑者走跑的累计步数。
  10. 如权利要求9所述的计步装置，其中，所述基频检测单元(400)包括：
    衰减滤波器，用于对每个高通滤波后的单轴加速度信号按从低频到高频衰减程度递增的方式进行衰减处理；
    计算单元，用于由下述公式求出所述衰减滤波器输出的信号的自相关函数ρ(τ)：
    Figure PCTCN2014001179-appb-100002
    其中，a(n)为该信号的第n个值，N为该信号的预定长度，且0≤n＜N，τ为延迟时间，ρ(τ)为该信号的归一化自相关函数；
    基频获得单元，用于求出ρ(τ)的最大值所对应的τ的值，并且输出该τ值的倒数作为所述高通滤波后的单轴加速度信号的基频。
  11. 如权利要求9所述的计步装置，其中，所述计步单元(800)包括加速度信号能量计算单元，用于计算所述各个单轴加速度信号的能量，并且，
    如果各个单轴加速度信号的能量相差不大，则所述计步单元(800)对各轴所对应的去除干扰极值点后的加速度信号极值点的数目进行平均，以该平均数作为本轮计步过程所获得的走跑步数；或者，如果各个单轴加速度信号的能量相差较大，则所述计步单元(800)根据其中能量最大的单轴加速度信号所对应的去除干扰极值点后的加速度信号极值点的数目来确定本轮计步过程所获得的走跑步数。
  12. 一种计算机程序，包括计算机可读代码，当所述计算机可读代码在服务器上运行时，导致所述服务器执行根据权利要求1-8中的任一个所述的计步方法。
  13. 一种计算机可读介质，其中存储了如权利要求12所述的计算机程序。
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