CN109126101A - Cycling energy consumption calculation system and method - Google Patents
Cycling energy consumption calculation system and method Download PDFInfo
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- CN109126101A CN109126101A CN201811273573.6A CN201811273573A CN109126101A CN 109126101 A CN109126101 A CN 109126101A CN 201811273573 A CN201811273573 A CN 201811273573A CN 109126101 A CN109126101 A CN 109126101A
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B69/00—Training appliances or apparatus for special sports
- A63B69/16—Training appliances or apparatus for special sports for cycling, i.e. arrangements on or for real bicycles
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2220/00—Measuring of physical parameters relating to sporting activity
- A63B2220/50—Force related parameters
- A63B2220/54—Torque
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2230/00—Measuring physiological parameters of the user
- A63B2230/04—Measuring physiological parameters of the user heartbeat characteristics, e.g. ECG, blood pressure modulations
- A63B2230/06—Measuring physiological parameters of the user heartbeat characteristics, e.g. ECG, blood pressure modulations heartbeat rate only
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2230/00—Measuring physiological parameters of the user
- A63B2230/75—Measuring physiological parameters of the user calorie expenditure
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- Health & Medical Sciences (AREA)
- Physical Education & Sports Medicine (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Engineering & Computer Science (AREA)
- Epidemiology (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The invention discloses a kind of cycling energy consumption calculation systems, it is characterised in that: including heart rate acquisition device, the signal output end of the heart rate acquisition device is connected with microprocessor, and the output end of the microprocessor and the heart rate signal collection terminal of terminal are wirelessly connected；It further include torque sensor, the signal output end of the torque sensor connects bluetooth module, and the output end of the bluetooth module is connect with the terminal Bluetooth；The terminal is equipped with human-computer interaction module, and the human-computer interaction module is used for the terminal input data.The utility model has the advantages that the present invention utilizes heart rate value and moment values, in conjunction with these characteristics of human body's parameters of the height, weight and age of tester, cycling energy consumption model is established, the model is enable accurately to calculate very much the amount calories that tester consumes during cycling.
Description
Technical field
The present invention relates to kinergeties to consume calculating field, specifically, being a kind of cycling energy consumption calculation system
And its method.
Background technique
With the rapid development of modern society, the pressure of people in daily life is increasing, then releasing stress just
It is particularly important.Cycling is exactly a kind of good pressure reducing mode.As the rapid emergence of bicycle is shared in China, voluntarily
The bodybuilding function of vehicle movement also gradually embodies, and more and more people have been added in the troop of cycling.
Cycling belongs to outdoor sports, can improve the metabolism of human body, of great advantage to the exercise of tissue,
Simultaneously to the blood circulation system of human body, nervous system, respiratory system, endocrine system and immune system etc., can generate
Good stimulation.So reasonably carrying out cycling is helpful to personal physical and mental health, but this
" reasonable " guidance for needing science.In order to instruct people reasonably to carry out fitness campaign, it would be desirable to by exercise intensity and
Amount of exercise shows, in order to which people can timely understand oneself physical condition when riding, and according to these information tune
The movement of oneself is saved, to achieve the effect that fitness campaign.And most can the parameter of expressive movement intensity and amount of exercise be that movement disappears
Consumption.
Currently, the calculation method about energy consumption has very much, such as double mark water technologies (DLW), indirect calorimetry, heart rate
Writing-method [3] and acceleration transducer method and the united method of the two etc..Double mark water technologies and indirect calorimetry are due to operation
The reasons such as more complicated, at high cost are not suitable for being used to energy consumption of the real-time detection people under free living state.Heart rate record
Method reflects exercise intensity by the size of heart rate, and then calculates energy consumption, but the individual difference of heart rate is larger, with year
Age, weight, physical fitness and gender etc. have close relationship, so causing its resultant error larger [7] different people.Accelerate
The principle for spending meter method measurement body kinematics energy consumption is according to Newton mechanics law, and Brouha just first proposed in nineteen sixty
Human motion acceleration absolute value between the integral of time and the energy consumption of human motion have preferable linear relationship.But
It is that cycling is kept identical riding condition not represent and identical power is overcome to do work, such as is having wind and calm
In the case of keep identical acceleration human consumption energy affirm when it is different.
Disadvantage of the prior art is that: existing energy consumption calculation method has certain limitation, the energy of calculating
There are biggish errors for consumption.
Summary of the invention
Haves the defects that biggish error for the calculation method of above-mentioned existing energy consumption, the invention proposes one kind certainly
Sports energy consumption computing system of driving a vehicle and its method, the system can accurately measure out the body index of tester, and combine body special
Obtain out energy consumption.
In order to achieve the above object, technical scheme is as follows:
A kind of cycling energy consumption calculation system, key are: including heart rate acquisition device, the heart rate acquisition device
Signal output end be connected with microprocessor, the output end of the microprocessor is connected with wireless module, the wireless module and end
The heart rate signal collection terminal at end is wirelessly connected；
It further include torque sensor, the signal output end of the torque sensor connects bluetooth module, the bluetooth module
Output end is connect with the terminal Bluetooth；
The terminal is equipped with human-computer interaction module, and the human-computer interaction module is used for the terminal input data.
Using the above scheme, the heart rate signal of heart rate acquisition device collecting test person is transmitted to microprocessor and carries out signal
Send terminal after processing to；Torque sensor acquisition obtains the torque that applies to bicycle of leg, terminal combination heart rate, torque with
And age, height and the weight of tester carry out COMPREHENSIVE CALCULATING, obtain movement consumption.
Further, the heart rate acquisition device is fixedly mounted on finger folder, and the finger folder passes through heart rate acquisition dress
The data line set is connected with wrist strap, and the microprocessor and wireless module are installed in wrist strap.
Further, the heart rate acquisition device is PPG signal pickup assembly, and the PPG signal pickup assembly is by double light
Source LED and light intensity detector composition, double light source leds and light intensity detector are separately mounted on the inside of two fixture blocks for referring to folder.
Using the above scheme, double light source leds can emit infrared light and near infrared light, and infrared light and near infrared light penetrate finger tip
Afterwards, two kinds of light are received by the light intensity detector, receive two kinds of light signals is transferred to microprocessor and are calculated
And analysis.
Further, the wrist strap is grandrelle cover material composition, and device is equipped between two layers of waterproof cloth
Installation position and data cable channel, the microprocessor and wireless module are fixedly mounted on the device installation position, the heart rate
The data line of acquisition device is connected by the data cable channel of the wrist strap with the microprocessor.
Using the above scheme, heart rate acquisition device is mounted on finger folder, refers to that folder clamps the heart rate value that finger tip carries out tester
Acquisition.Due to that can sweat in tester's motion process, sweat once soaks wrist strap, will affect microprocessor and wireless module
Service life, so wrist strap is made of waterproof material.
Further, the torque sensor is mounted on the axis at bicycle bottom bracket, and the bluetooth module is also installed
On the axis at bicycle bottom bracket, the torque sensor is connect by data line with the bluetooth module.
Using the above scheme, torque sensor is mounted on the axis of bicycle, is conducive to directly and accurately obtain leg
The size for the torque that portion applies bicycle.Bluetooth module is also mounted on axis simultaneously, makes bluetooth module and moment sensing
The problem of device follows middle shaft rotation together, and data line winding will not occur.During cycling, axis when people steps on foot-operated
Can stress, when axis stress can generate extremely subtle deformation under torsion, and torque sensor passes through some thin of measurement axis surface
Micro- deformation, torque size of the available people when trampling, and measurement accuracy is very high.
The specific structure of torque sensor and specific mounting means are the prior art, and " a kind of built-in motor is electronic certainly for patent
Disclose very detailed in driving axis moment sensing system ", therefore this will not be repeated here.
Further, the terminal be smart phone, the human-computer interaction module be touch screen, for input data with
And display data information.
Using the above scheme, touch screen can be used as input module and display module, can meet display data and input simultaneously
The function of information.
A kind of method of cycling energy consumption calculation system, key are: the following steps are included:
S1: cycling energy consumption model is established:
EE=β1H+β2W+β3A+β4HR+β5M
Wherein, the human motion energy that EE is tester consumes, and H indicates the height of tester, β1Indicate that kinergety disappears
Regression coefficient between consumption and height, W indicate the weight of tester, β2Indicate the recurrence system between kinergety consumption and weight
Number, A indicate the age of tester, β3Indicate kinergety consumption and the regression coefficient between the age, HR indicates every point of tester
The average heart rate of clock, β4It indicates kinergety consumption and the regression coefficient between heart rate, M indicates mean force per minute in real time
Square, β5Indicate kinergety consumption and the in real time regression coefficient between torque；
S2: the regression coefficient β of cycling energy consumption model is calculated1、β2、β3、β4And β5Value；
S3: cycling energy consumption model is saved in the terminal；
S4: terminal obtains height, weight and the age of tester by human-computer interaction interface；
S5: the heart rate value under tester's motion state is obtained by heart rate acquisition device；
S6: average torque per minute in tester's motion process is obtained by torque sensor；
S7: the energy consumption of tester's movement is calculated according to cycling energy consumption model.Using the above scheme, will
Height, weight and the age of tester joined in energy consumption model, and combine torque and heart rate data, make the meter of the model
It is more accurate to calculate result.
Regression coefficient β in step S2 in cycling energy consumption model1、β2、β3、β4And β5Calculation method is as follows:
S21: the quadratic loss function of cycling energy consumption model are as follows:
S22: it since the extreme point of Q is the point that local derviation is zero, to each regression coefficient derivation in quadratic loss function, obtains
To equation group:
S23: by carrying out the experiment of n group, the matrix of n group EE composition is obtained
N group human body personalizing parameters form matrix
And the matrix of regression coefficient composition
S24: simplified to equation group using the matrix in step S3: X ' X β=X ' EE；
S25: the characteristics of passing through matrix operation on the both sides of equation while multiplying the inverse matrix of X ' X respectively, obtains β=X-1EE；
Wherein, the value of EE and X obtains in the experiment of n group, it can thus be concluded that the value of coefficient matrix β.
The utility model has the advantages that the present invention utilizes heart rate value and moment values, in conjunction with these people of the height, weight and age of tester
Body characteristics parameter establishes cycling energy consumption model, and the model is enable accurately to calculate very much tester certainly
The amount calories consumed in driving motion process.
Detailed description of the invention
Fig. 1 is that system of the invention constitutes block diagram；
Fig. 2 is the assembling schematic diagram of heart rate acquisition device；
Fig. 3 is the assembling schematic diagram of torque sensor；
Fig. 4 is flow chart of the method for the present invention；
Fig. 5 is regression analysis residual plot.
Specific embodiment
Below with reference to examples and drawings, the invention will be further described:
Embodiment:
As shown in Figure 1, a kind of cycling energy consumption calculation system, key are: including heart rate acquisition device 1, using
In the heart rate value of collecting test person；The signal output end of the heart rate acquisition device 1 is connected with microprocessor 2, the microprocessor 2
Output end be connected with wireless module 7, the heart rate signal collection terminal of the wireless module 7 and terminal 5 is wirelessly connected；It further include torque
Sensor 3, the signal output end of the torque sensor 3 connect bluetooth module 4, the output end of the bluetooth module 4 and the end
Hold 5 bluetooth connections；The terminal 5 is equipped with human-computer interaction module 6, and the human-computer interaction module 6 is used to input the terminal 5
Data.
As shown in Fig. 2, the heart rate acquisition device 1 is fixedly mounted on finger folder 8, the finger folder 8 is adopted by the heart rate
The data line of acquisition means 1 is connected with wrist strap 9, and the microprocessor 2 and wireless module 7 are installed in wrist strap；The heart rate is adopted
Acquisition means 1 are PPG signal pickup assembly, which is made of double light source leds and light intensity detector, described double
Light source led and light intensity detector are separately mounted on the inside of two fixture blocks for referring to folder 8.Double light source leds can emit infrared light with it is close red
Outer light, infrared light and near infrared light receive two kinds of light by the light intensity detector, by receive two kinds of light through after finger tip
Line signal is transferred to microprocessor and is calculated and analyzed.
In the present embodiment, the wrist strap 9 is grandrelle cover material composition, and device is equipped between two layers of waterproof cloth
Installation position and data cable channel, the microprocessor 2 and wireless module 7 are fixedly mounted on the device installation position, the heart
The data line of rate acquisition device 1 is connected by the data cable channel of the wrist strap 9 with the microprocessor 2.In the wrist strap 9
In be additionally provided with battery, the battery be microprocessor 2, wireless module 7, PPG signal pickup assembly power supply, and the battery be charging lithium
Battery.
As shown in figure 3, the torque sensor 3 is mounted on the axis at bicycle bottom bracket, the bluetooth module 4 is also pacified
On the axis at bicycle bottom bracket, the torque sensor 3 is connect by data line with the bluetooth module 4.This implementation
In example, the model SEMPU T4.3 of the torque sensor 3.
Preferably, the human-computer interaction module 6 is touch screen when the terminal is smart phone, it to be used for input data
And display data information.When the terminal is computer, the human-computer interaction module 6 is made of display and keyboard, mouse.
As shown in figure 4, a kind of method of cycling energy consumption calculation system, key are: the following steps are included:
S1: cycling energy consumption model is established:
EE=β1H+β2W+β3A+β4HR+β5M
Wherein, the human motion energy that EE is tester consumes, and H indicates the height of tester, β1Indicate that kinergety disappears
Regression coefficient between consumption and height, W indicate the weight of tester, β2Indicate the recurrence system between kinergety consumption and weight
Number, A indicate the age of tester, β3Indicate kinergety consumption and the regression coefficient between the age, HR indicates every point of tester
The average heart rate of clock, β4It indicates kinergety consumption and the regression coefficient between heart rate, M indicates mean force per minute in real time
Square, β5Indicate kinergety consumption and the in real time regression coefficient between torque；
S2: the regression coefficient β of cycling energy consumption model is calculated1、β2、β3、β4And β5Value；
S3: cycling energy consumption model is stored in terminal 5；
S4: terminal 5 obtains height, weight and the age of tester by human-computer interaction interface 6；Wherein, height, weight
It can be filed in terminal 5 in advance by tester with the age, when test recalls archives, and the archives can be at any time by filing
Person updates；
S5: the heart rate value under tester's motion state is obtained by heart rate acquisition device 1；
S6: average torque per minute in tester's motion process is obtained by torque sensor 3；
S7: the energy consumption of tester's movement is calculated according to cycling energy consumption model.
Regression coefficient β in cycling energy consumption model1、β2、β3、β4And β5Calculation method is as follows:
S21: the quadratic loss function of cycling energy consumption model are as follows:
S22: it since the extreme point of Q is the point that local derviation is zero, to each regression coefficient derivation in quadratic loss function, obtains
To equation group:
S23: by carrying out the experiment of n group, the n group data for testing acquisition are as follows:
Obtain the matrix of n group EE composition
N group human body personalizing parameters form matrix
And the matrix of regression coefficient composition
S24: simplified to equation group using the matrix in step S3: X ' X β=X ' EE；
S25: the characteristics of passing through matrix operation on the both sides of equation while multiplying the inverse matrix of X ' X respectively, obtains β=X-1EE；
Wherein, the value of EE and X obtains in the experiment of n group, it can thus be concluded that the value of coefficient matrix β.
Carried out 11 groups of experiments, the volunteer's personalizing parameters and heart rate of the subject of acquisition, will wherein one group of data it is defeated
Enter into matlab and regression analysis carried out to it, operation result is as shown in the table:
| Coefficient R2 | Statistic F | Probability p | Estimation error variance | |
| stats | 0.9938 | 233.8043 | 0.0000 | 0.2230 |
First correlation coefficient r for being classified as the regression equation in table2, the value closer to 1 indicate regression equation it is more significant；The
Two values are statistic F, and F shows that more greatly regression equation is more significant；Third value is the corresponding P of statistic (probability) value, as P < α
It is refusal H0, (α indicates significance, and when default 0.05) value is for regression model establishment；4th value is evaluated error side
Difference.
Data in upper table meet above three condition, show human energy expenditure and height, weight, the age, heart rate and
There are linear relationships between torque.
Residual analysis is carried out to the data and conceptual data again, residual values are the difference knots of actual observation value and regression forecasting value
Fruit, operation result are as shown in Figure 5.The both ends of every line segment indicate the confidence interval of the point in residual plot, and white line indicates zero point, circle
Point indicates residual error.Distance of the residual error of data from zero point as can be seen from Fig., if residual error and zero point are leaned on closely to mean that by this
The difference of value and actual value that model calculates is just smaller.When the confidence interval of the residual error of point each in residual plot includes zero point,
This illustrates the fitting initial data that the regression model can be relatively good.It obviously can be seen that zero point is all contained in setting for residual error by Fig. 5
Believe in section.
Claims (8)
1. a kind of cycling energy consumption calculation system, it is characterised in that: including heart rate acquisition device (1), heart rate acquisition dress
The signal output end for setting (1) is connected with microprocessor (2), and the output end of the microprocessor (2) is connected with wireless module (7),
The wireless module (7) and the heart rate signal collection terminal of terminal (5) are wirelessly connected；
It further include torque sensor (3), the signal output end of the torque sensor (3) connects bluetooth module (4), the bluetooth mould
The output end of block (4) and the terminal (5) bluetooth connection；
The terminal (5) is equipped with human-computer interaction module (6), and the human-computer interaction module (6) is used to input the terminal (5)
Data.
2. cycling energy consumption calculation system according to claim 1, it is characterised in that: the heart rate acquisition device
(1) it is fixedly mounted on finger folder (8), the finger folder (8) is connected with wrist strap by the data line of the heart rate acquisition device (1)
(9), the microprocessor (2) and wireless module (7) are installed in wrist strap.
3. cycling energy consumption calculation system according to claim 2, it is characterised in that: the heart rate acquisition device
It (1) is PPG signal pickup assembly, which is made of double light source leds and light intensity detector, double light sources
LED and light intensity detector are separately mounted on the inside of two fixture blocks for referring to folder (8).
4. cycling energy consumption calculation system according to claim 2, it is characterised in that: the wrist strap (9) is bilayer
Waterproof cloth is constituted, and device installation position and data cable channel, the microprocessor (2) are equipped between two layers of waterproof cloth
It is fixedly mounted on the device installation position with wireless module (7), the data line of the heart rate acquisition device (1) passes through the wrist
The data cable channel of band (9) is connected with the microprocessor (2).
5. cycling energy consumption calculation system according to claim 1, it is characterised in that: the torque sensor (3)
It is mounted on the axis at bicycle bottom bracket, the bluetooth module (4) is also mounted on the axis at bicycle bottom bracket, the power
Square sensor (3) is connect by data line with the bluetooth module (4).
6. cycling energy consumption calculation system according to claim 1, it is characterised in that: the terminal (5) is intelligence
Mobile phone, the human-computer interaction module (6) are touch screen, for input data and display data information.
7. a kind of method of cycling energy consumption calculation system, it is characterised in that: the following steps are included:
S1: cycling energy consumption model is established:
EE=β1H+β2W+β3A+β4HR+β5M
Wherein, the human motion energy that EE is tester consumes, and H indicates the height of tester, β1Indicate kinergety consumption and body
Regression coefficient between height, W indicate the weight of tester, β2Indicate the regression coefficient between kinergety consumption and weight, A table
Show the age of tester, β3Indicate kinergety consumption and the regression coefficient between the age, HR indicates that tester is per minute and puts down
Equal heart rate, β4It indicates kinergety consumption and the regression coefficient between heart rate, M indicates average torque per minute, β in real time5It indicates
Regression coefficient between kinergety consumption and in real time torque；
S2: the regression coefficient β of cycling energy consumption model is calculated1、β2、β3、β4And β5Value；
S3: cycling energy consumption model is stored in terminal (5)；
S4: terminal (5) obtains height, weight and the age of tester by human-computer interaction interface (6)；
S5: the heart rate value under tester's motion state is obtained by heart rate acquisition device (1)；
S6: average torque per minute in tester's motion process is obtained by torque sensor (3)；
S7: the energy consumption of tester's movement is calculated according to cycling energy consumption model.
8. the method for cycling energy consumption calculation system according to claim 7, it is characterised in that: in step S2 voluntarily
Regression coefficient β in vehicle sports energy consumption model1、β2、β3、β4And β5Calculation method is as follows:
S21: the quadratic loss function of cycling energy consumption model are as follows:
S22: since the extreme point of Q is the point that local derviation is zero, to each regression coefficient derivation in quadratic loss function, the side of obtaining
Journey group:
S23: by carrying out the experiment of n group, the matrix of n group EE composition is obtained
N group human body personalizing parameters form matrix
And the matrix of regression coefficient composition
S24: simplified to equation group using the matrix in step S3: X ' X β=X ' EE；
S25: the characteristics of passing through matrix operation on the both sides of equation while multiplying the inverse matrix of X ' X respectively, obtains β=X-1EE；
Wherein, the value of EE and X obtains in the experiment of n group, it can thus be concluded that the value of coefficient matrix β.
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|---|---|---|---|
| CN201811273573.6A CN109126101B (en) | 2018-10-30 | 2018-10-30 | Method of bicycle motion energy consumption calculation system |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811273573.6A CN109126101B (en) | 2018-10-30 | 2018-10-30 | Method of bicycle motion energy consumption calculation system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110457995A (en) * | 2019-06-26 | 2019-11-15 | 山东师范大学 | A kind of human motion amount estimation method based on computer vision and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107049287A (en) * | 2017-06-19 | 2017-08-18 | 重庆邮电大学 | A kind of monitoring system moved for health-care bicycle |
| CN108211268A (en) * | 2018-01-25 | 2018-06-29 | 武汉中体智美科技有限公司 | Exercise load monitoring and sports fatigue method for early warning and system based on training data |
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2018
- 2018-10-30 CN CN201811273573.6A patent/CN109126101B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107049287A (en) * | 2017-06-19 | 2017-08-18 | 重庆邮电大学 | A kind of monitoring system moved for health-care bicycle |
| CN108211268A (en) * | 2018-01-25 | 2018-06-29 | 武汉中体智美科技有限公司 | Exercise load monitoring and sports fatigue method for early warning and system based on training data |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110457995A (en) * | 2019-06-26 | 2019-11-15 | 山东师范大学 | A kind of human motion amount estimation method based on computer vision and system |
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