CN109718528B - Identity recognition method and system based on motion characteristic parameters - Google Patents
Identity recognition method and system based on motion characteristic parameters Download PDFInfo
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- CN109718528B CN109718528B CN201811432800.5A CN201811432800A CN109718528B CN 109718528 B CN109718528 B CN 109718528B CN 201811432800 A CN201811432800 A CN 201811432800A CN 109718528 B CN109718528 B CN 109718528B
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Abstract
The invention discloses an identity recognition method and system based on motion characteristic parameters, which are used for generating a personal identity file aiming at a sporter by acquiring a characteristic vector of the sporter in a motion state and establishing an identity characteristic model of the sporter through data learning, so that the sporter can accurately acquire the identity file and select a proper motion state according to the identity file.
Description
Technical Field
The invention relates to the technical field of motion state monitoring and biological identification, in particular to an identity identification method and system based on motion characteristic parameters.
Background
Focusing on exercise health, monitoring and learning health data in real time has become one of the focuses of people on pursuing healthy life in daily life. In the prior art, various physiological data of a human body are collected through various wearable devices, and various current physiological parameters of a user are mastered in real time through monitoring data, so that the user can adjust the motion state in time conveniently.
However, some wearable devices in daily life may be used by different users, physiological data of different users are different, and collected data can only be used as reference, so that users without professional health knowledge cannot accurately judge and select exercise states suitable for themselves. On the other hand, historical data recorded by the wearable device cannot be judged whether the data are from the same user, and therefore a health report generated based on the historical data may not be accurate.
Disclosure of Invention
The invention aims to provide an identification method and system based on motion characteristic parameters, which aims at overcoming the defects in the prior art, generates a personal identification file aiming at a sporter by acquiring a characteristic vector of the sporter in a motion state and establishing an identification characteristic model of the sporter through data learning, is favorable for the sporter to accurately acquire the identification file and select a proper motion state according to the identification file, and can identify the sporter through the identification characteristic model by acquiring motion data of the sporter.
In order to achieve the purpose, the invention adopts the technical scheme that: an identity recognition method based on motion characteristic parameters comprises the following steps:
acquiring motion characteristic data of an individual in a plurality of time periods;
respectively calculating a motion characteristic vector of each time period according to the motion characteristic data;
constructing an identity model of the individual according to the motion characteristic vector;
and comparing the motion characteristic vectors of the athletes in a certain time period with the identity model, and calculating the identity integrating degree.
Further, the one time period is a time used for one motion step.
Further, the sports characteristic data is data acquired by a sensor in a sports shoe during sports, and comprises the following steps: heart rate data and a plurality of pressure data sets generated by different parts of the sole of the foot on the sports shoe.
Further, the calculating the motion feature vector includes:
calculating the heart rate c of the relative step frequency according to the heart rate data0,c0Pulse _ rate × T, where T is the time taken for one exercise stride, and pulse _ rate is the heart rate data;
generating a plurality of dynamic graphs of pressure data as a function of the time period from the plurality of pressure data sets;
respectively selecting a pressure peak value, a half-peak value time width and relative time of appearance of different curve peak values from each dynamic curve graph;
and respectively calculating the average impulse and the relative impulse according to the pressure peak value and the half-peak value time width, and normalizing the pressure transition time.
Further, the step of constructing the identity model of the individual according to the motion feature vector comprises:
calculating to obtain a plurality of motion characteristic vectors of the same individual in a plurality of different time periods;
accumulating the motion characteristic vectors of different time periods, and calculating the average value of the motion characteristic vectors as an initial identity model;
and acquiring a new motion characteristic vector, recalculating the average value of the motion characteristic vector, and updating the identity model.
Further, the calculating identity engagement degree comprises:
respectively collecting and calculating motion characteristic vectors of two feet of an athlete needing identity verification in a certain time period, and respectively comparing the motion characteristic vectors with an identity model to respectively obtain the fitting degree of the two feet;
substituting the fitting degree of the two feet into a formula:
wherein q is the total degree of engagement, qlIs the degree of fit of the left foot, qrThe degree of fit of the right foot.
In addition, to achieve the above object, the present invention further provides an identification system based on motion characteristic parameters, including:
the data acquisition module is used for acquiring motion characteristic data of an individual in a plurality of time periods;
the first data processing module is used for respectively calculating a motion characteristic vector of each time period according to the motion characteristic data;
the model building module is used for building an identity model of the individual according to the motion characteristic vector;
and the second data calculation module is used for comparing the motion characteristic vectors of the athletes in a certain time period with the identity model and calculating the identity integrating degree.
Further, the first data processing module includes:
the data analysis unit is used for generating a plurality of dynamic curve graphs of the pressure data changing along with the time period according to the plurality of pressure data sets;
the first calculation unit is used for respectively calculating the average impulse and the relative impulse according to the time width of the pressure peak value and the half-peak value selected from the dynamic curve chart;
a second calculation unit for normalizing the pressure transit time according to the dynamic graph.
Further, the model building module comprises:
the initial model establishing unit is used for accumulating the motion characteristic vectors in different time periods and calculating an average value as an initial identity model;
and the model updating unit is used for acquiring a new motion characteristic vector, recalculating the average value of the motion characteristic vector and updating the identity model.
On the other hand, the invention also provides an intelligent sports shoe which comprises a data acquisition module of the identification system based on the sports characteristic parameters, wherein the data acquisition module comprises a pressure sensor and a heart rate sensor which are arranged in the intelligent sports shoe.
The invention has the advantages that:
the invention can identify the identity of the sporter by collecting the physiological data of the sporter in the motion state, and can ensure that the identity can be continuously accurately tracked according to the identity identification even if the motion characteristics of the sporter change;
the invention establishes the sports file and the identity characteristic file of the sporter by collecting the data from the sensors on the sports shoes, and updates and adjusts the identity characteristic model aiming at the updating change of the sports data in real time, thereby facilitating the real-time monitoring of the sports state of the user and the adjustment of the optimal sports exercise mode according to the model.
Drawings
For a more complete understanding of the objects, features and advantages of the present invention, reference is now made to the following detailed description of the preferred embodiments of the invention, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic flow chart illustrating a method for identification based on motion data feature parameters according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a process of calculating motion feature vectors according to the present invention;
FIG. 3 is a schematic flow chart illustrating the process of building an identity model according to the present invention;
FIG. 4 is a flowchart illustrating a process of calculating identity engagement according to an embodiment of the present invention;
FIG. 5 is a dynamic graph of pressure data over a time period according to one embodiment;
FIG. 6 is a functional block diagram of an identification system based on motion data characteristic parameters;
FIG. 7 is a block diagram of a first data processing module;
FIG. 8 is a block diagram of a model building module in accordance with an embodiment.
Detailed Description
The technical solution of the present invention is described below with reference to the accompanying drawings and specific embodiments.
The technical scheme of this application embodiment is applied to intelligent sports shoes, and is especially applied to the intelligent sports shoes who installs the sensor, contains data acquisition module 101 in the intelligent sports shoes, specifically, this data acquisition module 101 for set up in pressure sensor and heart rate sensor in the intelligent sports shoes. Usually, the intelligent sports shoes are composed of a pair of shoes, each shoe contains the same sensor type, the sports shoes described in the embodiment are composed of two shoes with the same composition structure, each shoe is internally provided with 3 pressure sensors and 1 heart rate measuring sensor, and the preferred pressure sensors are fixed at the bottom of the shoes and used for collecting pressure data generated by different parts of the soles during sports. It should be noted that, in this embodiment, 3 pressure sensors and 1 heart rate measuring sensor are provided in each sports shoe as an example, and the practical application is not limited to this form, and may be modified as required, but all should be regarded as belonging to the protection scope of the present invention.
Referring to fig. 1-4, the present invention provides an identification method based on motion characteristic parameters, comprising the following steps:
s101, acquiring motion characteristic data of an individual in a plurality of time periods. The athlete usually takes periodic strides of the right and left feet during the exercise, and the pressure data changes generated on the sole by each stride are also periodic, so in this embodiment, a time period is the time taken for one exercise stride and the time taken for the athlete to take one stride.
The data that this embodiment gathered are the data that come from all sensors in the sports shoes gather, including 3 pressure sensor on a foot gather 3 group pressure data sets that different positions of sole produced to the sports shoes, come from the heart rate data of heart rate measurement sensor.
S102, respectively calculating motion characteristic vectors of each time period according to the motion characteristic data. The method specifically comprises the following steps:
s121, according to the heart rate data pulse _ rate acquired in the step S101, calculating the heart rate c of the relative step frequency0,c0Pulse _ rate × T, where T is the time used for one motion step.
S122, respectively generating a plurality of dynamic graphs of pressure data changing along with the time period according to the acquired pressure data sets from the 3 pressure sensors. Fig. 5 is a dynamic graph showing the pressure data varying with time period in this embodiment. Wherein, the 3 curves are dynamic curves of the pressure measured by a set of 3 pressure sensors on one sole and changing along with the time period respectively.
S123 respectively selecting pressure peak values p1, p2 and p3 collected by the corresponding pressure sensors in a time period, pressure half-peak time widths t11, t22 and t33 of the sensors, and relative times t12 and t13 of peak values among the three sensors from each dynamic graph.
S124, constructing a motion feature vector in a time period according to the motion feature data:
(c0, c1, c2, c3, c4, c5, c6, c7, c8, c9), and the vector S is a ten-dimensional coordinate vector, wherein:
c0 isHeart rate versus stride frequency;
c1 is the average impulse, which is related to the weight and the step frequency of the sporter, and the calculation method is as follows:
c1＝(p1·t11+p2·t22+p3·t33)/3；
c2, c3 and c4 are relative impulses and are related to the walking posture of the sporter, and the calculation method comprises the following steps:
c5, c6, c7, c8 and c9 are related to the foot shape and gait of an individual by the following calculation method:
s103, constructing the identity model of the individual according to the motion characteristic vector. The method specifically comprises the following steps:
s131 constructs motion feature vectors for several different time periods according to the calculation process of step S102, and preferably, to ensure the accuracy of modeling, at least 100 feature vectors should be constructed by repeated sampling.
S132, accumulating the sample motion characteristic vectors of all different time periods, and calculating the average value of the sample motion characteristic vectors as an initial identity model.
S133 after the initial identity model is built, new data are generated in each movement, new movement characteristic vectors are obtained through the data in the same calculation mode, the new movement characteristic vectors are added into the sample movement characteristic vectors to recalculate the average value of the movement characteristic vectors, the identity model is updated, and the identity model is recorded as S0.
S104, comparing the motion characteristic vectors of the athletes in a certain time period with the identity model, and calculating the identity integrating degree. In particular, the amount of the solvent to be used,
after the identity model is established, when a new sporter is authenticated, firstly, the sporter wears sports shoes to exercise to generate sports characteristic data, selects a sports characteristic vector S of one foot of the sporter in a certain time period, and substitutes the sports characteristic vector S into the following calculation formula of the sports fitting degree respectively to compare with the identity model:
and respectively obtaining the integrating degrees of the two feet according to the calculation mode: left foot fit qlDegree of fit q of right footr。
S142, substituting the fitting degree of the two feet into a formula:
wherein q is the total degree of engagement, qlIs the degree of fit of the left foot, qrThe degree of fit of the right foot. The smaller q is obtained, the higher the degree of engagement is, and in the specific implementation, a degree of engagement threshold can be set for identity recognition: q. q.s<10% of the total number of the cells are matched with each other by identity, q>10% are identity mutations. Meanwhile, the identity model can be gradually updated through time accumulation, so that the identity can be continuously and accurately tracked after the movement characteristics are gradually changed. The identity model may be updated by repeating step S102 and step S103 to recalculate the motion feature vector, or may be calculated by directly substituting into a motion feature vector update formula: s0 ═ S0 × 0.95+ S × 0.05
The motion characteristic parameter-based identity recognition method according to the embodiment of the present application is described in detail above with reference to fig. 1 to 5, and the motion characteristic parameter-based identity recognition system according to the embodiment of the present invention is described below with reference to fig. 6 to 8.
As shown in fig. 6, which is a schematic diagram of functional modules of an identification system based on motion data characteristic parameters, the identification system based on motion data characteristic parameters in this embodiment includes:
the data acquisition module 101 is used for acquiring motion characteristic data of an individual in a plurality of time periods;
the first data processing module 102 is configured to calculate a motion feature vector for each time period according to the motion feature data;
the model establishing module 103 is used for establishing an identity model of the individual according to the motion characteristic vector;
and the second data calculation module 104 is used for comparing the motion characteristic vectors of the athletes in a certain time period with the identity models to calculate the identity engagement degree.
Fig. 7 is a schematic diagram of a framework of a first data processing module 102, where the first data processing module 102 includes:
a data analysis unit 121, configured to generate a plurality of dynamic graphs of pressure data changing along with the time period according to the plurality of pressure data sets;
the first calculating unit 122 is configured to calculate an average impulse and a relative impulse according to the time widths of the pressure peak and the half-peak selected in the dynamic curve;
a second calculation unit 123 for normalizing the pressure transit time according to the dynamic graph.
Fig. 8 is a schematic diagram of a framework of the model building module 103, where the model building module 103 includes:
an initial model establishing unit 131, configured to accumulate motion feature vectors of different time periods, and calculate an average value as an initial identity model;
and the model updating unit 132 is configured to obtain a new motion feature vector, recalculate an average value of the motion feature vector, and update the identity model.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and additions can be made without departing from the method of the present invention, and these modifications and additions should also be regarded as the protection scope of the present invention.
Claims (7)
1. An identity recognition method based on motion characteristic parameters is characterized by comprising the following steps:
acquiring motion characteristic data of an individual in a plurality of time periods, wherein the motion characteristic data is acquired by a sensor in a sports shoe during sports, and the motion characteristic data comprises the following steps: heart rate data and a plurality of pressure data sets generated by different parts of the sole on the sports shoe;
respectively calculating a motion feature vector of each time period according to the motion feature data, wherein the calculating step comprises the following steps: calculating a heart rate c0 of a relative pace frequency according to the heart rate data, wherein c0 is pulse _ rate × T, T is a time used by one exercise stride, and pulse _ rate is the heart rate data;
generating a plurality of dynamic graphs of pressure data as a function of the time period from the plurality of pressure data sets;
respectively selecting a pressure peak value, a half-peak value time width and relative time of appearance of different curve peak values from each dynamic curve graph;
respectively calculating average impulse and relative impulse according to the time width of the pressure peak value and the half-peak value, and normalizing pressure transition time;
constructing an identity model of the individual according to the motion characteristic vector;
and comparing the motion characteristic vectors of the athletes in a certain time period with the identity model, and calculating the identity integrating degree.
2. The method of claim 1, wherein each time period is a time used for one motion step.
3. The identity recognition method based on the motion feature parameters of claim 1, wherein the step of constructing the identity model of the individual according to the motion feature vectors comprises:
calculating to obtain a plurality of motion characteristic vectors of the same individual in a plurality of different time periods;
accumulating the motion characteristic vectors of different time periods, and calculating the average value of the motion characteristic vectors as an initial identity model;
and acquiring a new motion characteristic vector, recalculating the average value of the motion characteristic vector, and updating the identity model.
4. The identity recognition method based on the motion feature parameters of claim 1, wherein the calculating the identity engagement degree comprises:
respectively collecting and calculating motion characteristic vectors of two feet of an athlete needing identity verification in a certain time period, and respectively comparing the motion characteristic vectors with an identity model to respectively obtain the fitting degree of the two feet;
substituting the fitting degree of the two feet into a formula: wherein q is the total fit, q l is the left foot fit, q r is the right foot fit.
5. An identification system based on motion characteristic parameters, the system comprising:
the data acquisition module is used for acquiring motion characteristic data of an individual in a plurality of time periods;
a first data processing module, configured to calculate a motion feature vector for each time period according to the motion feature data, where the first data processing module includes:
the data analysis unit is used for generating a plurality of dynamic curve graphs of the pressure data changing along with the time period according to the plurality of pressure data sets;
the first calculation unit is used for respectively calculating the average impulse and the relative impulse according to the time width of the pressure peak value and the half-peak value selected from the dynamic curve chart;
a second calculation unit for normalizing the pressure transit time according to the dynamic graph;
the model building module is used for building an identity model of the individual according to the motion characteristic vector;
and the second data calculation module is used for comparing the motion characteristic vectors of the athletes in a certain time period with the identity model and calculating the identity integrating degree.
6. The system according to claim 5, wherein the model building module comprises:
the initial model establishing unit is used for accumulating the motion characteristic vectors in different time periods and calculating an average value as an initial identity model;
and the model updating unit is used for acquiring a new motion characteristic vector, recalculating the average value of the motion characteristic vector and updating the identity model.
7. An intelligent sports shoe, characterized in that, the intelligent sports shoe comprises the identification system based on sports characteristic parameters of any one of claims 5-6, the identification system comprises a data acquisition module, and the data acquisition module comprises a pressure sensor and a heart rate sensor which are arranged in the intelligent sports shoe.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101773394A (en) * | 2010-01-06 | 2010-07-14 | 中国航天员科研训练中心 | Identification method and identification system using identification method |
| US8062864B2 (en) * | 2007-05-21 | 2011-11-22 | Alderbio Holdings Llc | Nucleic acids encoding antibodies to IL-6, and recombinant production of anti-IL-6 antibodies |
| WO2013129874A1 (en) * | 2012-03-02 | 2013-09-06 | Kim Min Soo | Game apparatus using strike sensing device |
| CN103886341A (en) * | 2014-03-19 | 2014-06-25 | 国家电网公司 | Gait behavior recognition method based on feature combination |
| CN105260718A (en) * | 2015-10-13 | 2016-01-20 | 暨南大学 | Gait identification method based on optical flow field |
| CN105809438A (en) * | 2016-03-17 | 2016-07-27 | 广东乐心医疗电子股份有限公司 | Identity recognition method, payment method and device for intelligent wearable equipment |
| CN106445101A (en) * | 2015-08-07 | 2017-02-22 | 飞比特公司 | Method and system for identifying user |
| CN108096807A (en) * | 2017-12-11 | 2018-06-01 | 丁贤根 | A kind of exercise data monitoring method and system |
| CN108304757A (en) * | 2017-06-16 | 2018-07-20 | 腾讯科技（深圳）有限公司 | Personal identification method and device |
| CN108836344A (en) * | 2018-04-26 | 2018-11-20 | 深圳市臻络科技有限公司 | Step-length cadence evaluation method and device and gait detector |
| US10556150B2 (en) * | 2011-03-31 | 2020-02-11 | Adidas Ag | Group performance monitoring system and method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5507788A (en) * | 1994-08-11 | 1996-04-16 | The Regents Of The University Of California | Method and apparatus for controlling skeletal muscle fatigue during electrical stimulation |
| US20140221160A1 (en) * | 2011-09-08 | 2014-08-07 | Paofit Holdings Pte. Ltd. | Sensor Device and System for Fitness Equipment |
| CN202795452U (en) * | 2012-09-28 | 2013-03-13 | 郝晓谷 | Fitness management system based on internet of things |
| CN107491682A (en) * | 2017-08-30 | 2017-12-19 | 努比亚技术有限公司 | Terminal unlocking optimization method, terminal and computer-readable recording medium |
-
2018
- 2018-11-28 CN CN201811432800.5A patent/CN109718528B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8062864B2 (en) * | 2007-05-21 | 2011-11-22 | Alderbio Holdings Llc | Nucleic acids encoding antibodies to IL-6, and recombinant production of anti-IL-6 antibodies |
| CN101773394A (en) * | 2010-01-06 | 2010-07-14 | 中国航天员科研训练中心 | Identification method and identification system using identification method |
| US10556150B2 (en) * | 2011-03-31 | 2020-02-11 | Adidas Ag | Group performance monitoring system and method |
| WO2013129874A1 (en) * | 2012-03-02 | 2013-09-06 | Kim Min Soo | Game apparatus using strike sensing device |
| CN103886341A (en) * | 2014-03-19 | 2014-06-25 | 国家电网公司 | Gait behavior recognition method based on feature combination |
| CN106445101A (en) * | 2015-08-07 | 2017-02-22 | 飞比特公司 | Method and system for identifying user |
| CN105260718A (en) * | 2015-10-13 | 2016-01-20 | 暨南大学 | Gait identification method based on optical flow field |
| CN105809438A (en) * | 2016-03-17 | 2016-07-27 | 广东乐心医疗电子股份有限公司 | Identity recognition method, payment method and device for intelligent wearable equipment |
| CN108304757A (en) * | 2017-06-16 | 2018-07-20 | 腾讯科技（深圳）有限公司 | Personal identification method and device |
| CN108096807A (en) * | 2017-12-11 | 2018-06-01 | 丁贤根 | A kind of exercise data monitoring method and system |
| CN108836344A (en) * | 2018-04-26 | 2018-11-20 | 深圳市臻络科技有限公司 | Step-length cadence evaluation method and device and gait detector |
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