CN109489683A - A kind of step-size estimation method, mobile terminal and storage medium - Google Patents
A kind of step-size estimation method, mobile terminal and storage medium Download PDFInfo
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- CN109489683A CN109489683A CN201710823540.3A CN201710823540A CN109489683A CN 109489683 A CN109489683 A CN 109489683A CN 201710823540 A CN201710823540 A CN 201710823540A CN 109489683 A CN109489683 A CN 109489683A
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- paces
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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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
Abstract
The present invention discloses a kind of step-size estimation method, mobile terminal and storage medium, is related to field of computer technology, can not in real time, accurately obtain paces length in the prior art to solve the problems, such as.The described method includes: establishing step-size estimation model and carrying out model training to estimate undetermined coefficient therein, wherein the step-size estimation model includes step intensity；The step intensity is the sum of the acquisition intensity of all data collection points in each paces；The acquisition intensity of each data collection point is equal to acceleration transducer and subtracts acceleration of gravity after the quadratic sum of the component of acceleration of three reference axis of rectangular coordinate system in space extracts square root again in the acceleration value on the data collection point；The height of user and the paces parameter measured are inputted into the step-size estimation model, to obtain the step-length of the user.
Description
Technical field
The present invention relates to field of computer technology, are situated between more particularly to a kind of step-size estimation method, mobile terminal and storage
Matter.
Background technique
With the development of economic science and technology and business model, personal navigation plays the role to become more and more important in life.
But since the function of building is more and more perfect, structure also becomes increasingly complex, and people are active in office building the most of the time, win
The interior of building such as object shop, library and megastore, GNSS (Global Navigation Satellite System,
Global Navigation Satellite System) can because building block with other factors and be interfered, and then cause satellite-signal it is poor, calmly
Position time length, position error increase, or even are unable to complete positioning.
The localization method of the signals such as GPS is not used mainly inertial navigation equipment to be utilized to realize at present, such as acceleration transducer, top
Spiral shell instrument etc..But since the consumer level inertial navigation sensor that such as mobile phone equipment is equipped at present belongs to low cost, coarse sensor, nothing
Method offers precise data, it is difficult to be directly used in positioning and navigation.And use the PDR for realizing positioning indirectly by paces behavior
(Pedestrian Dead Reckoning, pedestrian's dead reckoning) system is a kind of more effective method.
In PDR system, step-size estimation is one of core technology.Existing step-length acquisition methods mainly have: directly measurement,
The measuring tools such as ruler, infrared ray or ultrasonic wave can be relied on, but due to needing manual measurement or additional equipment, measure expense
Greatly, not flexible, it is difficult to meet the convenience demand of indoor positioning；Estimation indirectly, the incidence relation based on step-length and correlative factor,
To estimate step-length, existing indirect measurement method, the linear dependence etc. being based primarily upon between height and step-length, although can indirectly
To guarantee the real-time and dynamic of step-size estimation, but the accuracy in practical application is difficult to meet indoor positioning and navigation
It is required that.Thus, paces length how in real time, is accurately obtained, related fields there is no effective solution scheme.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of step-size estimation method, mobile terminal and storage medium, to
It solves the problems, such as in real time, accurately obtain paces length in the prior art.
On the one hand, the present invention provides a kind of step-size estimation method, comprising: establishes step-size estimation model and carries out model training
To estimate undetermined coefficient therein, wherein the step-size estimation model includes step intensity；The step intensity is each paces
In all data collection points the sum of acquisition intensity；The acquisition intensity of each data collection point is equal to acceleration transducer and exists
Acceleration value on the data collection point is opened again in the quadratic sum of the component of acceleration of three reference axis of rectangular coordinate system in space
Acceleration of gravity is subtracted after square；The height of user and the paces parameter measured are inputted into the step-size estimation model, to obtain
Take the step-length of the user.
Optionally, it establishes step-size estimation model and carries out model training to estimate that undetermined coefficient therein includes: according to body
The step-size estimation model is established in the high, influence of cadence and the step intensity to step-length；The step-size estimation model includes body
High cadence product term, height item, step intensity item and undetermined coefficient；According to known step-length, height, cadence and step intensity into
Row model training is with the undetermined coefficient in the determination step-size estimation model.
Further, it is described the height of user and the paces parameter measured are inputted into the step-size estimation model before,
The method also includes: the acceleration information of acquisition user's walking；Determine paces mark to obtain in the acceleration information
The acceleration information of single paces；Corresponding cadence and step intensity are determined according to the acceleration information of the single paces；Institute
State the height by user and the paces parameter that measures to input the step-size estimation model include: by the height of user, the step
Frequency and the step intensity input the step-size estimation model.
Optionally, described to determine paces mark to obtain the acceleration information packet of single paces in the acceleration information
It includes: the acceleration information is divided by several data groups, every number according to the fluctuation situation of the acceleration information at any time
According to a group corresponding single paces, the acceleration information of predeterminated position is the paces mark of the single paces in each data group
Will.
Optionally, it is described the acceleration information is divided into according to acceleration information fluctuation situation at any time it is several
A data group includes: to search the corresponding acceleration of each fluctuation according to the variation tendency of the acceleration information of adjacent data collection point
Wave crest；The acceleration information is divided into several data groups according to the acceleration wave crest, the acceleration in each data group
Wave crest is the paces mark of corresponding single paces.
Optionally, the number for the data collection point being spaced between two neighboring paces mark is greater than preset threshold.
Optionally, the preset threshold is in 0.2*facc between 0.5*facc, and wherein facc is the acceleration information
Sampling rate.
Optionally, after the acceleration information of the acquisition user walking, paces are determined in the acceleration information
Before indicating the acceleration information to obtain single paces, the method also includes: band logical filter is carried out to the acceleration information
Wave, the upper cut-off frequency of the bandpass filtering and the value range of lower-cut-off frequency are empirical value.
On the other hand, the present invention also provides a kind of mobile terminals, comprising: acceleration transducer, processor and memory；Institute
Acceleration transducer is stated for acquiring the acceleration information of user's walking；The memory is described for storing computer instruction
Processor is used to run the computer instruction of the memory storage, to realize any step-size estimation side provided by the invention
Method.
On the other hand, the present invention also provides a kind of computer readable storage medium, the computer readable storage medium is deposited
One or more program is contained, one or more of programs can be executed by one or more processor, to realize this
Any step-size estimation method provided is provided.
Step-size estimation method, mobile terminal and the storage medium that the embodiment of the present invention provides, can establish step-size estimation
Model simultaneously carries out model training to estimate undetermined coefficient therein, then will be described in the height of user and the cadence measured input
Step-size estimation model, to obtain the step-length of the user.Due to including customized step intensity in step-size estimation model,
Step intensity in linear model can be improved to underestimate step-size influences dynamics, to obtain better step-size estimation essence
Degree.
Detailed description of the invention
Fig. 1 is a kind of flow chart of step-size estimation method provided in an embodiment of the present invention；
Fig. 2 is a kind of process schematic of step-size estimation method provided in an embodiment of the present invention；
Fig. 3 is a kind of detail flowchart of step-size estimation method provided in an embodiment of the present invention.
Specific embodiment
Below in conjunction with attached drawing, the present invention is described in detail.It should be appreciated that specific embodiment described herein is only
To explain the present invention, the present invention is not limited.
As shown in Figure 1, the embodiment of the present invention provides a kind of step-size estimation method, comprising:
S11 establishes step-size estimation model and carries out model training to estimate undetermined coefficient therein, wherein the step-length
Estimate that model includes step intensity；The step intensity is the sum of the acquisition intensity of all data collection points in each paces；Often
It is straight in space that the acquisition intensity of a data collection point is equal to acceleration value of the acceleration transducer on the data collection point
The quadratic sum of the component of acceleration of three reference axis of angular coordinate system subtracts acceleration of gravity after extracting square root again；
The height of user and the paces parameter measured are inputted the step-size estimation model, to obtain the user by S12
Step-length.
The embodiment of the present invention provide step-size estimation method, step-size estimation model can be established and carry out model training with
It estimates undetermined coefficient therein, the height of user and the cadence measured is then inputted into the step-size estimation model, to obtain
The step-length of the user.Due to including customized step intensity in step-size estimation model, can improve in linear model
Step intensity underestimates step-size influences dynamics, to obtain better step-size estimation precision.
Specifically, due to step-size estimation towards crowd it is wide, individual difference is big, and influence factor is more, therefore simple mould
Type is difficult to the characteristics of describing paces length.For example, the height of different people, weight, walking habits etc. have the characteristics that it is respective, it is difficult to
Go to estimate their step-length with identical standard.In numerous factors for influencing step-length, height, cadence and step intensity are relatively
It is important.Wherein, height describes step-length with the linear positive variation relation of height, and cadence describes under identical height step-length with step
The relationship of frequency variation, step intensity describes under identical height and identical cadence, step-length with paces intensity of performance dynamic change
Relationship, thus accurately estimated result needs to comprehensively consider the influence factor of three, and simple linear relationship is also difficult to structure
Build out the accurate model between these factors and paces length.
In order to construct accurate model, optionally, step S11 establishes step-size estimation model and carries out model training to estimate
Undetermined coefficient therein may particularly include:
The step-size estimation model is established in influence according to height, cadence and the step intensity to step-length；The step-length
Estimate that model includes height cadence product term, height item, step intensity item and undetermined coefficient；
Model training is carried out according to known step-length, height, cadence and step intensity with the determination step-size estimation model
In undetermined coefficient.
For example, in one embodiment of the invention, can establish step-size estimation model:
SL=a*h*f+b*h+c*s+m (1)
Wherein, SL is step-length, and h is height, and f is cadence, and s is step intensity, and m is compensation factor, and a, b, c are respectively undetermined
Coefficient；Optionally, m can be 0 or the compensation factor of Normal Distribution；
Model training can be carried out according to known step-length, height, cadence and step intensity with the determination undetermined coefficient
A, the value of b, c.
In the specific implementation, it can be measured by experiment for data such as the step-length of model training, height, cadences.
The mass data that experiment measures is brought into the step-size estimation model containing undetermined coefficient, unknown parameter therein can be estimated
Out, to obtain complete step-size estimation model.
After the step-size estimation model for obtaining formula (1), in step s 12, it is only necessary to know that height, the cadence of user of user
And the paces parameter such as step intensity, i.e., the step-length that can estimate user using the step-size estimation model.
In order to obtain above-mentioned paces parameter, optionally, will the height of user and the paces parameter measured input described in
Before step-size estimation model, step-size estimation method provided in an embodiment of the present invention may also include that
Acquire the acceleration information of user's walking；
Determine paces mark to obtain the acceleration information of single paces in the acceleration information；
Corresponding cadence and step intensity are determined according to the acceleration information of the single paces；
Correspondingly, the height of user and the paces parameter measured are inputted the step-size estimation model can include:
The height of user, the cadence and the step intensity are inputted into the step-size estimation model.
Specifically, the acceleration of user's walking can be acquired by devices such as the acceleration transducers that is arranged on mobile terminal
Degree evidence.Then distinguish which data each paces include in collected acceleration information.Concrete principle is as follows: people is walking
Lu Shi, two legs staggeredly take a step to advance, and the acceleration of people can also do approximately periodic variation, should only one in single step paces
Wave crest has found corresponding single paces as long as distinguishing each period of change in collected acceleration information also.
In order to accurately find out each period of change in acceleration information, it can analyze in each period of change and accelerate degree
According to shared variation tendency, using the appearance of the variation tendency as the paces mark of each single paces.That is, each step
The appearance for cutting down mark means the generation of a paces event, that is, has stepped a step.
Optionally, in one embodiment of the invention, determine paces mark to obtain list in the acceleration information
The acceleration information of a paces is specific can include:
The acceleration information is divided into several data groups according to the fluctuation situation of the acceleration information at any time, often
A data group corresponds to a single paces, and the acceleration information of predeterminated position is the paces of the single paces in each data group
Mark.
Specifically, the acceleration information is divided into several according to the fluctuation situation of the acceleration information at any time
Data group can include:
The corresponding acceleration wave crest of each fluctuation is searched according to the variation tendency of the acceleration information of adjacent data collection point；
The acceleration information is divided into several data groups according to the acceleration wave crest, the acceleration in each data group
Spend the paces mark that wave crest is corresponding single paces.
It is more accurately grouped to exclude the random fluctuation of data to obtain, it can be according to people come the value of paces speed
Range enables the number for the data collection point being spaced between two neighboring paces mark be greater than preset threshold.
Further, after paces data being divided into single paces one by one, according to the acceleration of the single paces
Degree is according to the corresponding cadence of determination and step intensity can include:
Determine that the cadence f is equal to the inverse of the time interval of two neighboring paces mark；
Determine that the step intensity is the sum of the acquisition intensity of all data collection points in each paces；Each data
The acquisition intensity of collection point is equal to acceleration value of the acceleration transducer on the data collection point in rectangular coordinate system in space
The quadratic sum of the component of acceleration of three reference axis subtracts acceleration of gravity after extracting square root again.
By taking the corresponding step intensity of formula (1) as an example, it may be determined that step intensity s are as follows:
Wherein axt、ayt、aztRespectively acceleration transducer is in the corresponding X of t-th of data collection point, Y, the acceleration of Z axis
Degree, g are acceleration of gravity, and T is the number of acceleration information in each single paces.
For example, in one embodiment of the invention, the fluctuation feelings according to the acceleration information at any time
The acceleration information is divided into several data groups by condition can include:
In the acceleration information, it is determined for compliance with paces mark of the data point S [t] of the following conditions as a paces
Will:
S [t-3] < S [t-2] < S [t-1] < S [t] >=S [t+1] > S [t+2] > S [t+3],
Wherein, S [t] indicates the acceleration information of t-th of data collection point；
The acceleration information is divided into several data groups according to the paces mark.
As long as that is, finding 7 adjacent data collection points, wherein the numerical value of acceleration information is with acquisition time
First increases and then decreases can determine and a paces event have occurred.At this point, the downward trend that wave crest top data extends to both sides
Respectively take 3 sampled points.I.e. by wave crest forwardly and rearwardly both ends, all on a declining curve, the trend-monitoring range of every one end is all based on 3
A sampled point.Certainly, in other embodiments of the invention, it can according to need and change investigated adjacent data collection point
Number, the embodiment of the present invention do not limit this.Such as it can find with the presence or absence of 5,9 or the above-mentioned rule of more multiple coincidence
Data collection point then, to determine whether to produce paces mark, if paces event has occurred.The investigation number of data collection point
Mesh is related to the sample rate of user's stride frequency and acceleration information.
It, can not by actual conditions it is found that time interval when people walks between two steps should be in a reasonable range
It can be too fast.That is, it is contemplated that the physiological property of pedestrian's paces, it is impossible to two steps are stepped within the extreme time.Reflect
The quantity of the quantitative aspects of data collection point, the data collection point being spaced between two adjacent paces marks also should be one
In a reasonable range, therefore, if the interval between the paces mark that analysis obtains exceeds above-described zone of reasonableness,
Should consider that paces are flagged with may not look for pair, it is possible to some interference data are mistakenly considered paces mark, therefore right
Wrong paces mark is rejected in this way.
Optionally, in one embodiment of the invention, the data collection point being spaced between two neighboring paces mark
Number is greater than preset threshold, and the preset threshold is in 0.2*facc between 0.5*facc, and wherein facc is the acceleration degree
According to sampling rate.
In order to collected acceleration information more effectively be analyzed and extracted single paces data, further
, after the acceleration information of the acquisition user walking, determine paces mark to obtain list in the acceleration information
Before the acceleration information of a paces, step-size estimation method provided in an embodiment of the present invention further include:
Bandpass filtering, the upper cut-off frequency and lower-cut-off frequency of the bandpass filtering are carried out to the acceleration information
Value range be empirical value.Such as the value range of upper cut-off frequency can be 3Hz to 3.75Hz, the bandpass filtering
The value range of lower-cut-off frequency is 0.5Hz to 0.75Hz.It is most of in acceleration information after such bandpass filtering
Clutter interference can be filtered out, and be left the acceleration information with paces behavior correlated frequency region.
Step-size estimation method provided by the invention is described in detail below by specific embodiment.
As shown in Fig. 2, present embodiments providing a kind of long using the information such as height, cadence and step intensity estimation paces
The method of degree.Its core is that the acceleration information walked using acceleration transducer to pedestrian is acquired, and passes through the frequency of signal
Rate analyzes the frequency domain distribution characteristic for obtaining paces data, and suitable bandpass filter parameter logistic is arranged according to being filtered, obtains
Data relevant with paces.Then it recycles unimodal paces detection method to search out single step acceleration information, and is counted in time domain
Calculate cadence and step intensity (cadence is calculated in frequency domain will receive the influence of frequency resolution).Followed by cadence, step intensity
And height data derives multivariate regression models parameter.In subsequent process, by the cadence obtained every time, step intensity and
Height data, which substitutes among model, can be obtained by paces length estimation.As shown in figure 3, specifically may include that steps are as follows:
S301 utilizes the acceleration information of acceleration transducer acquisition pedestrian's paces.
S302 determines that paces signal highest is distributed in 2.7-3.75Hz by analyzing paces behavior frequency characteristic, minimum point
It is distributed in 0.5-1Hz；
S303, using paces frequency characteristic obtained in step S302, using bandpass filter, upper cut off frequency setting
For within the scope of 2.7-3.75Hz, lower limiting frequency is set as within the scope of 0.5-1Hz, acceleration information is filtered, is walked
Cut down the acceleration information in behavior correlated frequency region.
S304, based on the data processing in step S303, the acceleration information of paces can only go out within each paces period
An existing wave crest, thus it is taken based on unimodal paces behavioral value method, it finds and meets data point S [t] work claimed below
For the generation mark of paces event, i.e. paces mark meets following condition:
S [t-3] < S [t-2] < S [t-1] < S [t] >=S [t+1] > S [t+2] > S [t+3],
Wherein S [t] indicates the acceleration information of t moment, and considers the characteristic of paces event, between two paces events
Every no less than n=0.4*faccA data point, wherein facc refers to the sample frequency of acceleration transducer；
S305 is split acceleration information using paces mark, obtains the acceleration information of single paces；
S306, the calculation formula of cadence are as follows:Wherein Δ t is single paces duration, can be by step S305
In obtained single step paces data obtain.
S307, the calculation formula of step intensity are as follows:Wherein axt, ayt, azt points
Not Wei t-th of time point acceleration transducer X, Y, the acceleration value of Z axis, g is gravity acceleration value.
S308, the multivariate regression models of paces length estimate are as follows:
SL=a*h*f+b*h+c*s+m (1)
Wherein a, b, c are parameter factors, and h is height, and f is cadence, and s is step intensity, and m is the compensation of Normal Distribution
The factor.
S309 substitutes into the actual length of known cadence, step intensity, the height of pedestrian and the Walk in formula (1)
Model carries out model training, and the parameters of model in formula (1) are calculated using multiple linear regression analysis method, determine final mould
Type, such as the parameters of model are calculated using minimum recurrence square law, keep the mean square error of result minimum, obtains model
Final argument value.
S310 will obtain in real time cadence and step intensity, and combine the model of pedestrian's height input type (1) in step S306
Obtain the estimated value of step-length.
Step-size estimation method provided in this embodiment establishes the step-size estimation mould based on height, cadence and step intensity
Type.Wherein, height parameter describes step-length with the linear positive variation relation of height, and cadence describes under identical height, step-length with
The relationship of cadence variation, step intensity describe under identical height and identical cadence, and step-length becomes with the dynamic of paces intensity of performance
Change relationship.These three variables of height, cadence and step intensity are introduced in model, and impart different weight parameters, still
Different heights have paces length very big influence under identical cadence, and above-mentioned model does not only carry out three simple
Linear combination, and comprehensively consider the connection between influence and three of the three for step-length, introduce cadence and height product
Cross term, and certain amendment and compensation are carried out to model using the compensation factor of Normal Distribution.
Corresponding step-size estimation model is established according to above-mentioned step-size estimation method and carries out corresponding step-size estimation, it is every
Parameter and estimation result can be as shown in table 1.Influence due to height to step-length is significant, further can carry out segmentation table to model
Show: when height is greater than 170cm, using one group of a, b, c parameter above in table 1, when height is less than 170cm, under in table 1
One group of face a, b, c parameter, to obtain more accurate step-size estimation.
Table 1
Correspondingly, the embodiment of the present invention also provides a kind of mobile terminal, comprising: acceleration transducer, processor and deposit
Reservoir；
The acceleration transducer is used to acquire the acceleration information of user's walking；
The memory is used to run the computer of the memory storage for storing computer instruction, the processor
Instruction to realize any step-size estimation method of previous embodiment offer, therefore is also able to achieve corresponding technical effect, above
Detailed description has been carried out, details are not described herein again.
Correspondingly, the embodiment of the present invention also provides a kind of computer readable storage medium, the computer-readable storage
Media storage has one or more program, and one or more of programs can be executed by one or more processor, with
Realize any step-size estimation method that previous embodiment provides, therefore be also able to achieve corresponding technical effect, above into
Detailed description is gone, details are not described herein again.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the device that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or device institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or device.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in a storage medium
In (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal device (can be mobile phone, computer, clothes
Business device, air conditioner or the network equipment etc.) execute method described in each embodiment of the present invention.
Although for illustrative purposes, the preferred embodiment of the present invention has been disclosed, those skilled in the art will recognize
It is various improve, increase and replace be also it is possible, therefore, the scope of the present invention should be not limited to the above embodiments.
Claims (10)
1. a kind of step-size estimation method characterized by comprising
It establishes step-size estimation model and carries out model training to estimate undetermined coefficient therein, wherein the step-size estimation model
Including step intensity；The step intensity is the sum of the acquisition intensity of all data collection points in each paces；Each number
It is equal to acceleration value of the acceleration transducer on the data collection point in rectangular coordinate system in space according to the acquisition intensity of collection point
Three reference axis component of acceleration quadratic sum extract square root again after subtract acceleration of gravity；
The height of user and the paces parameter measured are inputted into the step-size estimation model, to obtain the step-length of the user.
2. the method according to claim 1, wherein establishing step-size estimation model and carrying out model training to estimate
Undetermined coefficient therein includes:
The step-size estimation model is established in influence according to height, cadence and the step intensity to step-length；The step-size estimation
Model includes height cadence product term, height item, step intensity item and undetermined coefficient；
Model training is carried out in the determination step-size estimation model according to known step-length, height, cadence and step intensity
Undetermined coefficient.
3. according to the method described in claim 2, it is characterized in that, described that the height of user and the paces parameter measured is defeated
Before entering the step-size estimation model, the method also includes:
Acquire the acceleration information of user's walking；
Determine paces mark to obtain the acceleration information of single paces in the acceleration information；
Corresponding cadence and step intensity are determined according to the acceleration information of the single paces；
It is described to include: by the height of user and the paces parameter measured the input step-size estimation model
The height of user, the cadence and the step intensity are inputted into the step-size estimation model.
4. according to the method described in claim 3, it is characterized in that, it is described in the acceleration information determine paces mark with
The acceleration information for obtaining single paces includes:
The acceleration information is divided into several data groups, every number according to the fluctuation situation of the acceleration information at any time
According to a group corresponding single paces, the acceleration information of predeterminated position is the paces mark of the single paces in each data group
Will.
5. according to the method described in claim 4, it is characterized in that, the fluctuation feelings according to the acceleration information at any time
The acceleration information is divided into several data groups by condition
The corresponding acceleration wave crest of each fluctuation is searched according to the variation tendency of the acceleration information of adjacent data collection point；
The acceleration information is divided into several data groups according to the acceleration wave crest, the acceleration wave in each data group
Peak is the paces mark of corresponding single paces.
6. according to the method described in claim 5, it is characterized in that, the data collection point being spaced between two neighboring paces mark
Number be greater than preset threshold.
7. according to the method described in claim 6, it is characterized in that, the preset threshold 0.2*facc to 0.5*facc it
Between, wherein facc is the sampling rate of the acceleration information.
8. according to the method described in claim 3, it is characterized in that, it is described acquisition user walking acceleration information after,
Before determining acceleration information of the paces mark to obtain single paces in the acceleration information, the method also includes:
Bandpass filtering, the upper cut-off frequency of the bandpass filtering and taking for lower-cut-off frequency are carried out to the acceleration information
Value range is empirical value.
9. a kind of mobile terminal characterized by comprising acceleration transducer, processor and memory；
The acceleration transducer is used to acquire the acceleration information of user's walking；
The memory refers to for storing computer instruction, the computer that the processor is used to run the memory storage
It enables, to realize step-size estimation method described in any item of the claim 1 to 8.
10. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage have one or
Multiple programs, one or more of programs can be executed by one or more processor, to realize in claim 1 to 8
Described in any item step-size estimation methods.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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