CN104570840A - Preset stepping strength detection method, processor and motion detection equipment - Google Patents
Preset stepping strength detection method, processor and motion detection equipment Download PDFInfo
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Abstract
The invention discloses a preset stepping strength detection method. The method comprises the following steps: determining a first data parameter A1 and a second data parameter A2 through triaxial data of a user sent by utilizing a first motion state gravity acceleration sensor; calculating according to a formula that V is equal to (45*A1+77*A2) to acquire vector original data V; using a formula that Vector is equal to X1 (V0-V7)+X2(V1-V6)+X3(V2-V5)+X4(V3-V4) according to continuous eight vector original data to acquire vector data Vector; extracting peak value data in the vector data Vector; recording an average value of the peak value data as preset stepping strength. Because the preset stepping strength is generated when the user moves in the first motion state, and therefore, the preset stepping strength detection method is relatively high in accuracy.
Description
Technical field
The application relates to sports equipment technical field, more particularly, relates to a kind of default take a step dynamics detection method, processor and motion detection device.
Background technology
Due to the raising of quality of life, the life of people is entered at present gradually for the motion detection device detected user movement status information, but the motion detection function of traditional sports equipment is more single, usually step number is only had to detect, user's dynamics size used when stepping each step is got by gravity acceleration sensor when carrying out step number and detecting, conveniently described in subsequent descriptions, dynamics size is dynamics peak value, dynamics peak value presets the dynamics number of peaks of dynamics of taking a step close to one, be " step number " of user in motion process, therefore, preset if described the dynamics of taking a step to arrange excessive or too small " step number " that all can accurately not detect user, certainly, if the dynamics peak value of all generations all to be measured into the motion " step number " of described user, then count " step number " that " step number " will inevitably comprise some irregular actions generations, such as when " step number " that need counting user to walk within some day, " step number " that produce during marking time or riding during non-walking also can be added up interior.
Visible, the described precision being directly connected to described motion detection device detection user movement " step number " presetting dynamics size of taking a step, therefore, how to determine the described size presetting dynamics of taking a step accurately, become one of those skilled in the art's technical matters urgently to be resolved hurrily.
Summary of the invention
In view of this, the application provides a kind of and presets take a step dynamics detection method, processor and motion detection device, the default dynamics of taking a step that accurately can detect user movement information for providing.
To achieve these goals, the existing scheme proposed is as follows:
A kind of default dynamics detection method of taking a step, comprising:
Obtain three number of axle certificates of many groups continuously that user's Gravity accelerometer under the first motion state sends;
Calculate often organize three the number of axle according in three number of axle according to sum, be designated as the first data parameters A1;
Obtain the maximal value often organized in three number of axle certificates, be designated as the second data parameters A2;
Formula V=(45*A1+77*A2)/256 is adopted to calculate and often organize three number of axle according to mated vector raw data V according to often organizing three number of axle according to the first mated data parameters and the second data parameters;
Vector data Vector is calculated according to formula Vector=X1 (V0-V7)+X2 (V1-V6)+X3 (V2-V5)+X4 (V3-V4) by continuous eight vector raw data;
Extract the peak-data in described vector data Vector;
Calculate the mean value of all peak-data, be designated as and preset dynamics of taking a step;
Wherein, described X1, X2, X3 and X4 are preset weights.
Preferably, above-mentioned presetting is taken a step in dynamics detection method, and described weights X1 is 125, weights X2 is 114, weights X3 is 76, weights X4 is 38.
Preferably, above-mentioned presetting is taken a step in dynamics detection method, and the mean value of all peak-data of described calculating is designated as and presets dynamics of taking a step, comprising:
Calculate the mean value of all peak-data, in peak-data described in filtering, be greater than the peak-data of preset value with described average value difference value;
Calculate the mean value of remaining peak-data, be designated as and preset dynamics of taking a step.
A kind of processor, is connected with Gravity accelerometer, comprises:
Vector data computing module, for obtaining three number of axle certificates of many groups continuously that user's Gravity accelerometer under the first motion state sends, calculate often organize three the number of axle according in three number of axle according to sum, be designated as the first data parameters A1, obtain the maximal value often organized in three number of axle certificates, be designated as the second data parameters A2, formula V=(45*A1+77*A2)/256 is adopted to calculate and often organize three number of axle according to mated vector raw data V according to often organizing three number of axle according to the first mated data parameters and the second data parameters, vector data Vector is calculated according to formula Vector=X1 (V0-V7)+X2 (V1-V6)+X3 (V2-V5)+X4 (V3-V4) by continuous eight vector raw data,
Preset dynamics computing module of taking a step, extract the peak-data in described vector data Vector, calculate the mean value of all peak-data, be designated as and preset dynamics of taking a step;
Wherein, described X1, X2, X3 and X4 are preset weights.
Preferably, in above-mentioned processor, described weights X1 is 125, weights X2 is 114, weights X3 is 76, weights X4 is 38.
Preferably, in above-mentioned processor, described mean value calculation module, comprising:
Peak-data extraction module, for extracting the peak-data in described vector data Vector;
Effective mean value calculation module, for calculating the mean value of all peak-data, being greater than the peak-data of preset value with described mean value difference in peak-data described in filtering, calculating the mean value of remaining peak-data, is designated as and presets dynamics of taking a step.
A kind of motion detection device, comprising:
Storer, stores the default dynamics of taking a step of mating from different motion states in described storer;
Selector switch, transfers for the instruction of transferring inputted according to user the default dynamics of taking a step that instruction mates described in storer is transferred;
Detecting device, the default dynamics of taking a step for transferring according to described selector switch detects the motion step count information of user.
Preferably, above-mentioned motion detection device, comprising:
Described storer in store mate from different motion states, adopt above-mentioned a kind of storer presetting the default dynamics of taking a step that dynamics detection method obtains of taking a step.
Preferably, above-mentioned motion detection device, also comprises:
Processor disclosed in above-mentioned any one, described processor is connected with described storer, for detecting the default dynamics of taking a step that user's different motion state is mated, and preset dynamics of taking a step and the described dynamics of the taking a step motion state of mating of presetting is stored to storer by described.
Preferably, above-mentioned motion detection device, also comprises:
Adjuster, described adjuster is connected with described storer, for adjusting the size of the default dynamics of taking a step stored in described storer.
As can be seen from above-mentioned technical scheme, default dynamics detection method of taking a step disclosed in the present application, three number of axle certificates that during by moving under the first motion state according to user, Gravity accelerometer generates, generate vector data Vector, extract in described vector data Vector for representing that user is in motion process, often step the peak-data of the size of step power used, and using the mean value of described peak-data as presetting dynamics of taking a step.Visible, in method disclosed in the above embodiments of the present application, by calculating when user is with the first motion state motion, often step default the take a step dynamics of mean value as the first motion state of step dynamics used.Because described dynamics of taking a step of presetting generates with during the first motion state motion according to user, therefore there is higher precision.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only embodiments of the invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to the accompanying drawing provided.
Fig. 1 is a kind of process flow diagram presetting dynamics detection method of taking a step disclosed in the embodiment of the present application;
Fig. 2 is a kind of oscillogram presetting vector raw data and vector data in dynamics detection method of taking a step disclosed in the embodiment of the present application;
Fig. 3 is the disclosed process flow diagram presetting dynamics detection method of taking a step of another embodiment of the application;
The structural drawing of Fig. 4 a kind of processor disclosed in the embodiment of the present application;
The structural drawing of Fig. 5 motion detection device disclosed in the embodiment of the present application.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Fig. 1 is a kind of process flow diagram presetting dynamics detection method of taking a step disclosed in the embodiment of the present application.
See Fig. 1 and Fig. 2, movement locus detection method disclosed in the present application comprises:
Step S101: obtain three number of axle certificates of many groups continuously that user's Gravity accelerometer under the first motion state sends;
Described three number of axle are according to comprising: X-axis data abs (X), Y-axis data abs (Y) and Z axis data abs (Z).
Step S102: calculate often organize three the number of axle according in three number of axle according to sum, be designated as the first data parameters A1;
That is, this step can pass through formula A1=abs (X)+abs (Y)+abs (Z), calculates this group three number of axle according to the first corresponding data parameters A1;
Step S103: obtain the maximal value often organized in three number of axle certificates, be designated as the second data parameters A2;
That is, often organize three number of axle according to the maximal value in (abs (X), abs (Y), abs (Z)) by choosing in this step, using described maximal value as this group three number of axle according to the second corresponding data parameters A2.
Step S104: adopt formula V=(45*A1+77*A2)/256 to calculate and often organize three number of axle according to mated vector raw data V (as shown in waveform in Fig. 2 01) according to the first mated data parameters A1 and the second data parameters A2 according to often organizing three number of axle;
Wherein, described 45 and 77 and 256 is the weights in this formula;
Step S105: calculate vector data Vector (as shown in waveform in Fig. 2 02) according to formula Vector=X1 (V0-V7)+X2 (V1-V6)+X3 (V2-V5)+X4 (V3-V4) by continuous eight vector raw data;
It is to be noted, three number of axle are often organized according to all one can be generated with described three number of axle according to mated vector raw data in the above embodiments of the present application, such as, different according to the time often organizing three number of axle certificates got, can by temporally it divides into first to N group three number of axle certificate, described first is first to N vector raw data to N group three number of axle according to corresponding vector raw data, when calculating first vector data Vector0 in this step, described first vector raw data is applied to above-mentioned formula to the 8th vector raw data and obtains, when calculating second vector data Vector1, second vector raw data is applied to above-mentioned formula to the 9th vector raw data and obtains, wherein, in above-mentioned continuous eight vector raw data, first vector raw data is designated as V0, second vector raw data is designated as V1, the like, 8th vector raw data is designated as V7, wherein, X1 described in above-mentioned formula, X2, X3 and X4 is preset weights, and described X1, X2, the size of X3 and X4 is adjustable.
Step S106: extract the peak-data in described vector data Vector;
Described peak-data represents that user is when with the first motion state motion, the dynamics of taking a step of each step;
Step S107: the mean value calculating all peak-data, is designated as and presets dynamics of taking a step;
Namely described dynamics of taking a step of presetting can be used as default the take a step dynamics of motion detection device when detecting user movement information.
Wherein, first motion state of described user can be riding condition, running state or normal walking states etc.
Visible by method disclosed in the above embodiments of the present application, three number of axle certificates that during by moving under the first motion state according to user, Gravity accelerometer generates, generate vector data Vector, extract in described vector data Vector for representing that user is in motion process, often step the peak-data of the size of step power used, and using the mean value of described peak-data as presetting dynamics of taking a step.Visible, in method disclosed in the above embodiments of the present application, by calculating when user is with the first motion state motion, often step default the take a step dynamics of mean value as the first motion state of step dynamics used.Because described dynamics of taking a step of presetting generates with during the first motion state motion according to user, therefore there is higher precision.
Be understandable that, in the application's said method, when obtaining vector data by described continuous eight groups of vector raw data V according to above-mentioned formulae discovery, the size of weights X1, X2, X3, X4 in above-mentioned formula can set according to user's request, such as, in order to accurate measurement, the described weights X1 in the above embodiments of the present application can be set to 125, weights X2 can be set to 114, weights X3 can be set to 76, weights X4 can be set to 38.
Be understandable that, in method disclosed in above-described embodiment, if when user keeps with the first motion state motion always, the dynamics of taking a step can be preset accurately, but, user with first motion state motion determine described preset take a step dynamics time, irregular action may be there is, and make the peak-data now generated for there is larger fluctuation, finally the described precision presetting dynamics of taking a step is impacted, therefore, in order to avoid the problems referred to above, see Fig. 3, the described step S107 in the above embodiments of the present application can comprise:
Step S1071: the mean value calculating all peak-data, is greater than the peak-data of preset value with described average value difference value in peak-data described in filtering;
Step S1072: the mean value calculating remaining peak-data, is designated as and presets dynamics of taking a step.
In the above-mentioned methods, the peak-data produced due to irregular action must be fortuitous phenomena, it can not produce extreme influence to the size of described mean value, therefore, first time or after obtaining mean value, judge in described peak-data, whether to there is the peak-data (being greater than preset value with the difference of mean value) that there is notable difference with described mean value, if there is this peak-data, then show when this peak-data generates, the action of user is irregular action, this peak-data of filtering, recalculate mean value, therefore, even if user is when running with the first motion state, there is irregular action, adopt the method still can calculate and preset dynamics of taking a step accurately.
Be understandable that, corresponding to said method, disclosed herein as well is a kind of processor, both can use for reference mutually.See Fig. 4, disclosed in the embodiment of the present application, processor 1 comprises:
Vector data computing module 101, for obtaining three number of axle certificates of many groups continuously that user's Gravity accelerometer 0 under the first motion state sends, calculate often organize three the number of axle according in three number of axle according to sum, be designated as the first data parameters A1, obtain the maximal value often organized in three number of axle certificates, be designated as the second data parameters A2, formula V=(45*A1+77*A2)/256 is adopted to calculate and often organize three number of axle according to mated vector raw data V according to often organizing three number of axle according to the first mated data parameters and the second data parameters, vector data Vector is calculated according to formula Vector=X1 (V0-V7)+X2 (V1-V6)+X3 (V2-V5)+X4 (V3-V4) by continuous eight vector raw data,
Preset dynamics computing module 102 of taking a step, extract the peak-data in described vector data Vector, calculate the mean value of all peak-data, be designated as and preset dynamics of taking a step;
Wherein, described X1, X2, X3 and X4 are preset weights.
Preset corresponding to above-mentioned dynamics detection method of taking a step, the above-mentioned weights X1 of the application is 125, weights X2 is 114, weights X3 is 76, weights X4 is 38.
Corresponding with above-mentioned default dynamics detection method of taking a step, the described mean value calculation module 102 in the above embodiments of the present application can comprise:
Peak-data extraction module, for extracting the peak-data in described vector data Vector;
Effective mean value calculation module, for calculating the mean value of all peak-data, being greater than the peak-data of preset value with described mean value difference in peak-data described in filtering, calculating the mean value of remaining peak-data, is designated as and presets dynamics of taking a step.
Be understandable that, corresponding with said method and processor, disclosed herein as well is a kind of motion detection device, see Fig. 5, comprising:
Storer 2, stores the default dynamics of taking a step of mating from different motion states in described storer;
Selector switch 3, transfers for the instruction of transferring inputted according to user the default dynamics of taking a step that instruction mates described in storer is transferred;
Detecting device 4, the default dynamics of taking a step for transferring according to described selector switch detects the motion step count information of user.
Wherein, described detecting device 4 can be motion detection device main body conventional in prior art, and its testing process and principle are already present technical scheme in prior art, need not explain at this again.
See motion detection device disclosed in the embodiment of the present application, the default dynamics of taking a step matched from different motion states is provided with in described storer 2, user can select the default dynamics of taking a step of mating with this motion state when detecting the movable information of a certain motion state by selector switch 3, controls described detecting device 3 and presets dynamics movable information to user of taking a step detect according to this.
Be understandable that, corresponding with said method, the default dynamics of taking a step that the storer in the above embodiments of the present application stores can for the default dynamics of taking a step adopting the above embodiments of the present application any one default dynamics detection method of taking a step disclosed to obtain.
Be understandable that, corresponding with processor disclosed in the above embodiments of the present application, motion detection device disclosed in the above embodiments of the present application can also comprise:
Processor 1 disclosed in the above-mentioned any one of the application, described processor 1 is connected with described storer 2, for detecting the default dynamics of taking a step that user's different motion state is mated, and preset dynamics of taking a step and the described dynamics of the taking a step motion state of mating of presetting is stored to storer 2 by described.
Be understandable that, described storer 2, after getting default take a step dynamics and the described default motion state of taking a step corresponding to dynamics of described processor transmission, be the default dynamics data of taking a step of current acquisition by the original default dynamics Data Update of taking a step corresponding with this motion state.
Be understandable that, conveniently user simple manually setting can preset dynamics data of taking a step voluntarily, the application goes back in motion detection device disclosed in above-described embodiment, an adjuster can also be provided with, described adjuster is connected with described storer, for adjusting the size of the default dynamics of taking a step stored in described storer.
When user manually arrange preset take a step dynamics time, user can make in advance described presetting to be taken a step dynamics data setting at certain value, then with the first motion state walking, simultaneously from line item movable information (step number in such as motion process), then check that whether the movable information that described motion detection device obtains is consistent with the movable information that user oneself records, if fruit then shows that the default dynamics of taking a step that user manually sets can be used, otherwise continue the described default dynamics of taking a step of adjustment.
In this instructions, each embodiment adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar portion mutually see.
To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the present invention.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein can without departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.
Claims (10)
1. preset a dynamics detection method of taking a step, it is characterized in that, comprising:
Obtain three number of axle certificates of many groups continuously that user's Gravity accelerometer under the first motion state sends;
Calculate often organize three the number of axle according in three number of axle according to sum, be designated as the first data parameters A1;
Obtain the maximal value often organized in three number of axle certificates, be designated as the second data parameters A2;
Formula V=(45*A1+77*A2)/256 is adopted to calculate and often organize three number of axle according to mated vector raw data V according to often organizing three number of axle according to the first mated data parameters and the second data parameters;
Vector data Vector is calculated according to formula Vector=X1 (V0-V7)+X2 (V1-V6)+X3 (V2-V5)+X4 (V3-V4) by continuous eight vector raw data;
Extract the peak-data in described vector data Vector;
Calculate the mean value of all peak-data, be designated as and preset dynamics of taking a step;
Wherein, described X1, X2, X3 and X4 are preset weights.
2. according to claim 1 presetting is taken a step dynamics detection method, it is characterized in that, described weights X1 is 125, weights X2 is 114, weights X3 is 76, weights X4 is 38.
3. movement locus detection method according to claim 1, is characterized in that, the mean value of all peak-data of described calculating, is designated as and presets dynamics of taking a step, comprising:
Calculate the mean value of all peak-data, in peak-data described in filtering, be greater than the peak-data of preset value with described average value difference value;
Calculate the mean value of remaining peak-data, be designated as and preset dynamics of taking a step.
4. a processor, is characterized in that, is connected with Gravity accelerometer, comprising:
Vector data computing module, for obtaining three number of axle certificates of many groups continuously that user's Gravity accelerometer under the first motion state sends, calculate often organize three the number of axle according in three number of axle according to sum, be designated as the first data parameters A1, obtain the maximal value often organized in three number of axle certificates, be designated as the second data parameters A2, formula V=(45*A1+77*A2)/256 is adopted to calculate and often organize three number of axle according to mated vector raw data V according to often organizing three number of axle according to the first mated data parameters and the second data parameters, vector data Vector is calculated according to formula Vector=X1 (V0-V7)+X2 (V1-V6)+X3 (V2-V5)+X4 (V3-V4) by continuous eight vector raw data,
Preset dynamics computing module of taking a step, extract the peak-data in described vector data Vector, calculate the mean value of all peak-data, be designated as and preset dynamics of taking a step;
Wherein, described X1, X2, X3 and X4 are preset weights.
5. processor according to claim 4, is characterized in that, described weights X1 is 125, weights X2 is 114, weights X3 is 76, weights X4 is 38.
6. processor according to claim 4, is characterized in that, described mean value calculation module, comprising:
Peak-data extraction module, for extracting the peak-data in described vector data Vector;
Effective mean value calculation module, for calculating the mean value of all peak-data, being greater than the peak-data of preset value with described mean value difference in peak-data described in filtering, calculating the mean value of remaining peak-data, is designated as and presets dynamics of taking a step.
7. a motion detection device, is characterized in that, comprising:
Storer, stores the default dynamics of taking a step of mating from different motion states in described storer;
Selector switch, transfers for the instruction of transferring inputted according to user the default dynamics of taking a step that instruction mates described in storer is transferred;
Detecting device, the default dynamics of taking a step for transferring according to described selector switch detects the motion step count information of user.
8. motion detection device according to claim 7, is characterized in that, comprising:
Described storer in store mate from different motion states, any one presets the storer of the default dynamics of taking a step that dynamics detection method obtains of taking a step to adopt the claims 1-3.
9. motion detection device according to claim 7, is characterized in that, also comprises:
Processor disclosed in claim 4-6 any one, described processor is connected with described storer, for detecting the default dynamics of taking a step that user's different motion state is mated, and preset dynamics of taking a step and the described dynamics of the taking a step motion state of mating of presetting is stored to storer by described.
10. motion detection device according to claim 9, is characterized in that, also comprises:
Adjuster, described adjuster is connected with described storer, for adjusting the size of the default dynamics of taking a step stored in described storer.
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