CN104007822B - Motion recognition method and its device based on large database concept - Google Patents
Motion recognition method and its device based on large database concept Download PDFInfo
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Abstract
The invention discloses a kind of motion recognition method based on large database concept, comprise the following steps：S1, foundation include the database of multi-motion parameter；Exercise data during S2, collection user movement；S3, kinematic parameter in the exercise data collected in S2 and S1 databases contrasted, analysis judgement draws specific type of sports；S4, output judge the result of type of sports.The problem of amount can only be manually entered type of sports when the present invention fundamentally solves tradition wearing electronic equipment monitoring motion, based on the large database concept for storing various type of sports, user need to be only worn on diverse location when using the product with the technology by type of sports, and the user action automatic identification type of sports that system can be detected according to acceleration transducer and angular rate sensor simultaneously records the data of user's everything to make back-end data analysis application.
Description
Technical field
The present invention relates to portable electronic wearable device, specifically applied on electronics wearable device and based on big data
The motion recognition method in storehouse.
Background technology
As people's living standard is improved constantly, people are also constantly strengthening the attention degree of sport and body-building, more and more
People find time to be run, play ball, the body-building such as Yoga, but how many amounts of exercise is most suitableHow
It just can know that the energy consumed after oneself motionThese are all related to the health of itself, therefore in the market is occurred in that a lot
Related product, such as：Pedometer, intelligent watch, motion bracelet etc., these products can carry out motion monitoring, in the market this
All there is not enough intelligence or the monitoring of relative complex athletic performance need to could be carried out by host computer auxiliary operation setting in class product,
I.e. by being manually entered type of sports on wearing electronic product, but it is cumbersome, in scientific and technological growing today, it is clear that no
Enough intellectualities, thus urgently occur it is a kind of can the technology of automatic identification type of sports change this present situation.
The content of the invention
For above-mentioned technical problem, the present invention provides motion recognition method and its device based on large database concept.
The technical solution adopted by the present invention is：
A kind of motion recognition method based on large database concept, comprises the following steps：
S1, foundation include the database of multi-motion parameter；
Exercise data during S2, collection user movement；
S3, kinematic parameter in the exercise data collected in S2 and S1 databases contrasted, analysis judgement is drawn specifically
Type of sports；
S4, output judge the result of type of sports.
Further, database is parameter formation data when choosing a large amount of crowd movements by sampling in the step S1
Existing motion parameter data storehouse is directly quoted in storehouse
Further, the kinematic parameter based on different sexes, the age, body weight, height crowd, i.e., sex, the age,
Body weight, height are used as variable when judging type of sports.
Wherein, exercise data is the 3-axis acceleration Ax, Ay, Az and three shaft angles of multigroup different time points in the S2
Speed θ x, θ y, θ z.
Further, conversion process also is carried out with the data in matching database to S2 collection exercise datas in the step S3
Type.
The motion recognition method of the present invention also includes step S5, man-machine self study data renewal is carried out by networking, to increase
Plus new type of sports data are to database.
Specifically, the mode of output campaign types results is display output and/or voice output in the S4.
Another technical scheme of the present invention, i.e., a kind of wearing electronic installation of the above-mentioned motion recognition method of application, for wearing
Wear and the type that human body is moved detected when human body, it is characterised in that：Connect including a system processor and with system processor
Data storage, power module, acceleration transducer, the angular rate sensor connect, wherein, system processor is wearing electronics dress
The core processing center put, data storage stores the data of multi-motion parameter, acceleration transducer, angular rate sensor
The 3-axis acceleration Ax, Ay, Az and tri-axis angular rate θ x, θ y, θ z during human motion are detected respectively and will detect that data are anti-
Feeding system processor, the system processor is directed to after detection data progress analog-to-digital conversion and the kinematic parameter in data storage
Contrasted, discriminatory analysis draws the type of sports corresponding to the data detected；The power module supplies for other each modules
Electricity.
The wearing electronic installation of the present invention also includes a wireless networking communication module, the wireless networking communication module and system
Processor connects to be updated for the networking data of data storage.
Above-mentioned wearing electronic installation also includes the display module and/or voice module being connected with system processor.
The beneficial effects of the invention are as follows：
Amount can only be manually entered type of sports when the present invention fundamentally solves tradition wearing electronic equipment monitoring motion
Problem, based on the large database concept for storing various type of sports, user only need to be by motion when using the product with the technology
Type is worn on diverse location, and the user action that system can be detected according to acceleration transducer and angular rate sensor is certainly
Dynamic identification type of sports simultaneously records the data of user's everything to make back-end data analysis application.
Brief description of the drawings
The embodiment to the present invention is described further below in conjunction with the accompanying drawings.
Fig. 1 is the theory diagram of electronics wearable device of the present invention；
Fig. 2 is the motion recognition method flow chart of the invention based on large database concept；
Fig. 3 is acceleration transducer Data Detection illustraton of model；
Fig. 4 is angular rate sensor Data Detection illustraton of model；
Fig. 5 is software algorithm also original sensor detection curve figure.
Embodiment
Electronics wearable device of the present invention refers in particular to the equipment such as pedometer, intelligent watch, motion bracelet, its common spy
Property is portability, i.e., do not carried with user, using this characteristic, these equipment just can therewith be transported in user movement
Dynamic, based on this, amount of exercise, the movement locus of user is just synchronous with detection device, so the motion detection knot of detection device itself
Fruit detects substantially identical with the displacement needed for user.
As shown in figure 1, being the wearing electronic installation of the present invention, including a system processor 100 and and system processor
Data storage 200, power module 300, acceleration transducer 400, angular rate sensor 500, a display module for 100 connections
600 and voice module 700；
Wherein, system processor 100 is the core processing center of wearing electronic installation, the processing for data；Acceleration
Sensor 400, angular rate sensor 500 are respectively 3-axis acceleration sensor, gyroscope；
Data storage 200 stores the data of multi-motion parameter, acceleration transducer 400, angular rate sensor 500
The 3-axis acceleration Ax, Ay, Az and tri-axis angular rate θ x, θ y, θ z during human motion are detected respectively and will detect that data are anti-
Feeding system processor 100, the system processor 100 for detection data carry out analog-to-digital conversion after with data storage 200
Kinematic parameter contrasted, discriminatory analysis draws the type of sports corresponding to the data detected；
Display module 600 is display driving and corresponding terminal display screen, for showing testing result and other routines
Display content；
Voice module 700 is the loudspeaker that voice driven circuit and voice terminal are exported, for voice message testing result
And the voice output such as alarm；
The power module 300 is battery and corresponding power-supplying circuit, and low-voltage direct is provided for other each modules
Power input.
Because the species of motion is varied, product is difficult to prestore all fortune in data storage 200 when dispatching from the factory
The data of dynamic classification, therefore, wearing electronic installation of the invention is provided with a wireless networking communication module 800, the Wireless Networking
Communication module 800 is connected the networking data renewal for data storage 200 with system processor 100, can complete new in time
The data inputting of type of exercise.
The principle of the present invention is all made up of based on every kind of motion some fixed action groups, and all coherent sexual acts are all
It is made up of the monomer action decomposed one by one, the people of different building shape or height their movement locus when doing same movement
Curve is similar, and height that we can input according to user, body weight, sex, age are simulated with computer programmed algorithm
The curve movement database of one visual human close to user's bodily form, the athletic performance data point during product are used further according to user
Analysis result goes perfect amendment fantasy sport curve to be formed one and the Standard User athletic performance data of user closely
Storehouse, as long as user has acceleration module and angular rate measurement module on motion, product just can be by user when product is used
Each dynamic acceleration and angular speed changing value detection try out and be converted into numerical value, system processing further according to human action supervise
Survey identification technology algorithm by acceleration and angular speed changing value be reduced into human action curve movement again with database correct after
Visual human curve movement database contrast can draw the type of sports that user is currently carried out so that intelligence identify
The motion that user is engaged in.
With reference to shown in Fig. 2, it is a kind of motion recognition method based on large database concept of the present invention, comprises the following steps：
S1, foundation include the database of multi-motion parameter；
The database is that parameter when choosing a large amount of crowd movements by sampling forms database or directly quotes existing fortune
Dynamic parameter database, the kinematic parameter based on different sexes, the age, body weight, height crowd, i.e., sex, the age, body weight,
Height needs in first use input sex, age, body weight, height lamp ginseng as variable when judging type of sports
Count to determine the human parameters of user；
After product power-up initializing, the wireless synchronization clock date time, and acknowledging time；
Exercise data during S2, collection user movement；
The exercise data detected respectively by acceleration transducer 400, angular rate sensor 500 and come it is multigroup different when
Between the 3-axis acceleration Ax, Ay, Az and tri-axis angular rate θ x, θ y, θ z that put, see Fig. 3, Fig. 4；
S3, kinematic parameter in the exercise data collected in S2 and S1 databases contrasted, analysis judgement is drawn specifically
Type of sports；The dormancy if without motion, type of sports when this programme focuses principally on motion judges；
Because S2 collection exercise datas are analog electrical signal, and the kinematic parameter stored in database is binary number
According to, therefore the analog-to-digital conversion process of data need to be also monitored with analog-digital converter integrated in processor in system with coupling number
According to the binary data types in storehouse, in order to which judgement is analyzed under same data type；
As shown in figure 5, in the present embodiment, multigroup exercise data after changing is using the time into transverse coordinate axis variable, with three
Axle acceleration Ax, Ay and tri-axis angular rate θ x, θ y, θ z are longitudinal axis variable, constitute curve movement, pass through curve movement
Each group coordinate points are contrasted with the corresponding sports curvilinear coordinate in database, when the matching degree between data is in the default margin of tolerance
It is interior, that is, judge detected type of sports as the motion corresponding to this group of coordinate data in database；Wherein acceleration A z and Ax,
Ay is in functional relation, and invention software algorithm uses Ax, and Ay replaces the judgement of Az participation type of sports；
S4, output judge the result of type of sports, and wearing electronic equipment is just may be such that after the result of type of sports is drawn
The calculating of kinergety is carried out, and type of sports is manually entered every time like that without entering legacy equipment, in addition, type of sports
As a result the way of output directly perceived is display output or voice output, when amount of exercise or exceeded time, can send different to user
Often prompting, such as glistens or sound of blowing a whistle；
S5, when run into have the new motion for not having data in database when, carry out man-machine self study data by networking and update,
To increase new type of sports data to database；
Specifically, DHM is man-machine self-studying mode three-dimensional path curve；
DAUTO is automatic identification model three-dimensional path curve, that is, the user movement curve detected；
When： |DHM-DAUTO| <Δ D, when judge recognize successfully；
Wherein, Δ D：The margin of tolerance；
ΔD = （1/N）|（½cosθ.k*ΔAx1.k *ΔT1.k ²+½cosθ.k *ΔAy1.k *ΔT1.k ²+
½cosθ.k *ΔAz1.k *ΔT1.k ²）-（½cosθ.k *ΔAx2.k *ΔT2.k ²+½cosθ.k *ΔAy2.k *Δ
T2.k ²+½cosθ.k *ΔAz2.k *ΔT2.k ²）| (k=1..N)；
N：Gathered data quantity；
ΔT：The duration of motion process；
ΔAx, ΔAy,ΔAz:Acceleration difference；
Δθx, Δθy, Δθz:Angular speed difference；
Application example：Play basketball motion monitoring
Product using the technology is worn on ankle or footwear by user, and sensor can be by after user starts basketball movement
The curve movement of the actions such as pin/ankle is run, jump detects that system processor 100 monitors identification technology algorithm pair by human action
The user's human body data and large database concept data that the curve movement and system of this time monitoring are algorithmically generated do recognizer to sentence
The type of sports of user is determined to play basketball, and this all exercise datas are recorded and by human action monitoring knowledge user
Other technique algorithm calculates this user and has run how many step, run how much rice, jumped how many times（Shooting, held ball）, consume it is many
The data such as few heat simultaneously send abnormal prompt (posture is not to the risk that sprains one's foot) etc. during running take-off, and user is not only
Oneself motion state and consumption of calorie are solved and have also known and oneself robbed ball several times, thrown basket etc. several times and increase the use entertaining of product
Property and Consumer's Experience sense.
The preferred embodiments of the present invention are the foregoing is only, the present invention is not limited to above-mentioned embodiment, as long as with
Essentially identical means realize that the technical scheme of the object of the invention is belonged within protection scope of the present invention.
Claims (9)
1. a kind of motion recognition method based on large database concept, it is characterised in that comprise the following steps：S1, foundation include a variety of fortune
The database of dynamic parameter, the kinematic parameter includes the specific kinematic parameter of some fixed sequences, the tool of fixed sequence
Body kinematic parameter is made up of some monomer action parameters decomposed；Exercise data during S2, collection user movement, it is described
Exercise data is the 3-axis acceleration Ax, Ay, Az and tri-axis angular rate θ x, θ y, θ z, Duo Zuyun of multigroup different time points in S2
Dynamic data are using the time as transverse coordinate axis variable, with 3-axis acceleration Ax, Ay, Az and tri-axis angular rate θ x, θ y, θ z for the longitudinal axis
Variable, constitutes curve movement；S3, kinematic parameter in the exercise data collected in S2 and S1 databases contrasted, analysis judges
Draw specific type of sports, i.e. each group coordinate points by curve movement and the monomer action parameter in database and fixed company
The specific kinematic parameter contrast of action is passed through, when the matching degree between data is in the default margin of tolerance, that is, judges what is detected
Type of sports is the motion corresponding to this group of coordinate data in database；S4, output judge the result of type of sports.
2. the motion recognition method according to claim 1 based on large database concept, it is characterised in that：Number in the step S1
It is that database is formed by parameter during sampling a large amount of crowd movements of selection or existing motion parameter data storehouse is directly quoted according to storehouse.
3. the motion recognition method according to claim 2 based on large database concept, it is characterised in that：The kinematic parameter base
In different sexes, the age, body weight, height crowd, i.e. sex, age, body weight, height is used as change when judging type of sports
Amount.
4. the motion recognition method according to claim 1 based on large database concept, it is characterised in that：In the step S3 also
Conversion process is carried out with the data type in matching database to S2 collection exercise datas.
5. the motion recognition method according to claim 1 based on large database concept, it is characterised in that：It also includes step
S5, carry out man-machine self study data by networking and update, to increase new type of sports data to database.
6. the motion recognition method according to claim 1 based on large database concept, it is characterised in that：Fortune is exported in the S4
The mode of dynamic types results is display output and/or voice output.
7. a kind of wearing electronic installation of any motion recognition methods of application claim 1-6, for being worn on during human body
The type that detection human body is moved, it is characterised in that：The data being connected including a system processor and with system processor are deposited
Reservoir, power module, acceleration transducer, angular rate sensor, wherein, at core of the system processor for wearing electronic installation
Reason center, data storage stores the data of multi-motion parameter, and the kinematic parameter includes the tool of some fixed sequences
Body kinematic parameter, the specific kinematic parameter of fixed sequence is made up of some monomer action parameters decomposed, and acceleration is passed
Sensor, angular rate sensor detect the 3-axis acceleration Ax, Ay, Az and tri-axis angular rate θ x, θ y, θ z during human motion respectively
And data feedback will be detected to system processor, the system processor is carried out after analog-to-digital conversion and data storage for detection data
The specific kinematic parameter of monomer action parameter and fixed sequence in device is contrasted, and discriminatory analysis draws the number detected
According to corresponding type of sports；The power module is other each module for power supply.
8. wearing electronic installation according to claim 7, it is characterised in that：It also includes a wireless networking communication module,
The wireless networking communication module is connected the networking data renewal for data storage with system processor.
9. wearing electronic installation according to claim 7, it is characterised in that：It also includes one be connected with system processor
Display module and/or voice module.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5788655A (en) * | 1994-09-07 | 1998-08-04 | Omron Corporation | Exercise amount measuring device capable of displaying the amount of exercise to be performed further |
| CN102221369A (en) * | 2011-04-29 | 2011-10-19 | 韩铮 | Gesture recognizing method and device of ball game and gesture auxiliary device |
| CN102804238A (en) * | 2011-12-15 | 2012-11-28 | 北京英福生科技有限公司 | Exercise reminding device and system |
| CN103345627A (en) * | 2013-07-23 | 2013-10-09 | 清华大学 | Action recognition method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW201028886A (en) * | 2009-01-22 | 2010-08-01 | Asustek Comp Inc | Method and system for identifying 3D motion |
-
2014
- 2014-05-30 CN CN201410240299.8A patent/CN104007822B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5788655A (en) * | 1994-09-07 | 1998-08-04 | Omron Corporation | Exercise amount measuring device capable of displaying the amount of exercise to be performed further |
| CN102221369A (en) * | 2011-04-29 | 2011-10-19 | 韩铮 | Gesture recognizing method and device of ball game and gesture auxiliary device |
| CN102804238A (en) * | 2011-12-15 | 2012-11-28 | 北京英福生科技有限公司 | Exercise reminding device and system |
| CN103345627A (en) * | 2013-07-23 | 2013-10-09 | 清华大学 | Action recognition method and device |
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Effective date of registration: 20180508 Address after: 528400 1, Daling industrial area, Daling village, Torch Development Zone, Zhongshan, Guangdong Patentee after: Guangdong Heng Heng Liang Liang Technology Co., Ltd. Address before: 528400 Zhongshan, Guangdong, Zhongshan four Road 88, No. 88, Shang Feng financial business center 2 hall 19. Patentee before: Zhongshan Yeshm Interconnection Technology Co., Ltd. |