CN109830278B - Anaerobic exercise fitness recommendation method and device, anaerobic exercise equipment and storage medium - Google Patents
Anaerobic exercise fitness recommendation method and device, anaerobic exercise equipment and storage medium Download PDFInfo
- Publication number
- CN109830278B CN109830278B CN201910035532.1A CN201910035532A CN109830278B CN 109830278 B CN109830278 B CN 109830278B CN 201910035532 A CN201910035532 A CN 201910035532A CN 109830278 B CN109830278 B CN 109830278B
- Authority
- CN
- China
- Prior art keywords
- sides
- anaerobic
- motion
- human body
- equipment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention relates to an anaerobic exercise fitness recommendation method and device, anaerobic exercise equipment and a storage medium. The method comprises the following steps: acquiring movement tracks of two sides of the anaerobic exercise equipment when a user uses the anaerobic exercise equipment for body building; inputting the motion tracks of the two sides of the anaerobic motion equipment into a preset neural network model to obtain the action postures of the two sides of the human body, wherein the preset neural network model comprises the corresponding relation between the motion tracks of the two sides of the human body and the action postures of the two sides of the human body; calculating relative errors of the action postures of the two sides of the human body according to the action postures of the two sides of the human body, and comparing the relative errors with corresponding relative error thresholds; and if the relative error is larger than the relative error threshold, outputting a prompt message, wherein the prompt message is used for prompting the user to correct the current wrong action posture. By the method, the user can know whether the current action posture is correct or not in time, and the interactive experience between the user and the anaerobic exercise equipment is improved.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to an anaerobic exercise fitness recommendation method and device, anaerobic exercise equipment and a storage medium.
Background
With the improvement of living standard, the importance of exercise in daily life of people is increasingly prominent. The body-building exercise mainly comprises aerobic exercise and anaerobic exercise, a large number of aerobic exercise devices are transformed and upgraded intelligently, such as data interconnection, intelligent analysis and the like, and the anaerobic exercise is an essential exercise in the body-building exercise, and the data interconnection, recording and intelligent analysis of the devices are also very important.
In the conventional technology, when data transmission is performed on anaerobic exercise equipment, generally, the transmitted data are one-dimensional data, including displacement, ambient temperature, ambient humidity and the like of the equipment. By analyzing the data, the number of exercises, the amount of exercise, the amount of consumption, and the like of the exerciser can be calculated.
However, the data transmitted by the data analysis method is single, and the interaction between the user and the anaerobic exercise equipment is not intelligent enough.
Disclosure of Invention
Therefore, it is necessary to provide a method and a device for recommending anaerobic exercise fitness, an anaerobic exercise device and a storage medium for solving the problems that data transmitted by the data analysis method are single, interaction between a user and the anaerobic exercise device is not intelligent enough, and the like.
An anaerobic exercise fitness recommendation method, the method comprising:
acquiring movement tracks of two sides of anaerobic exercise equipment when a user uses the anaerobic exercise equipment to build a body; wherein the motion trail is obtained from three-dimensional data of each position in the motion process of the anaerobic motion equipment;
inputting the motion tracks of the two sides of the anaerobic motion equipment into a preset neural network model to obtain the action postures of the two sides of the human body, wherein the preset neural network model comprises the corresponding relation between the motion tracks of the two sides of the human body and the action postures of the two sides of the human body;
calculating relative errors of the action postures of the two sides of the human body according to the action postures of the two sides of the human body, and comparing the relative errors with corresponding relative error thresholds;
and if the relative error is larger than the relative error threshold, outputting a prompt message, wherein the prompt message is used for prompting the user to correct the current error action posture.
In one embodiment, the acquiring the movement tracks of the two sides of the anaerobic exercise device when the user uses the anaerobic exercise device for body-building includes:
in the movement process of the anaerobic movement equipment, acquiring the angular acceleration, the angular velocity and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position;
calculating the angle of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position by utilizing a preset angle conversion algorithm according to the angular acceleration and the angular velocity of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position;
and obtaining the movement tracks of the two sides of the anaerobic movement equipment according to the angle and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position.
In one embodiment, the inputting the motion trajectories of the two sides of the anaerobic motion device into a preset neural network model to obtain the motion postures of the two sides of the human body includes:
identifying the types of the motion tracks on the two sides of the anaerobic motion equipment by adopting the neural network model;
and obtaining the action postures of the two sides of the human body according to the corresponding relation between the types of the motion tracks of the two sides of the anaerobic motion equipment and the action postures of the two sides of the human body in the neural network model.
In one embodiment, the calculating the relative error of the motion postures of the two sides of the human body comprises:
calculating relative errors of the motion tracks of the two sides of the human body;
and determining the relative error of the motion tracks of the two sides of the human body as the relative error of the action postures of the two sides of the human body.
In one embodiment, the method further comprises:
taking a plurality of training motion trajectory groups as input of an initial neural network model, taking training action posture groups corresponding to the training motion trajectory groups as output, and training the initial neural network model to obtain the neural network model;
the training motion track group comprises training motion tracks on two sides of the anaerobic motion equipment, and the training action posture group comprises training action postures on two sides of a human body.
In one embodiment, the method further comprises:
acquiring user information and temperature and humidity information of an environment, wherein the user information comprises at least one of the following items: a user's preferred exercise type, age, joint size, past medical history, heart rate, blood oxygen concentration, body temperature, historical exercise data;
and determining a fitness recommendation scheme matched with the user information and the temperature and humidity information of the environment.
In one embodiment, the method further comprises:
acquiring current physiological parameters of a user and a selected fitness scheme, wherein the physiological parameters comprise at least one of heart rate, blood oxygen concentration and body temperature, and the fitness scheme comprises standard physiological parameters;
judging whether the current physiological parameters of the user are matched with the standard physiological parameters in the fitness scheme;
and if not, outputting an early warning message, wherein the early warning message is used for instructing the user to modify the current fitness scheme.
An anaerobic exercise fitness recommendation device, the device comprising:
the trajectory acquisition module is used for acquiring the movement trajectories of two sides of the anaerobic exercise equipment when a user uses the anaerobic exercise equipment for body building; wherein the motion trail is obtained from three-dimensional data of each position in the motion process of the anaerobic motion equipment;
the determining module is used for inputting the motion tracks of the two sides of the anaerobic motion equipment into a preset neural network model to obtain the action postures of the two sides of the human body, and the preset neural network model comprises the corresponding relation between the motion tracks of the two sides of the human body and the action postures of the two sides of the human body;
the calculation module is used for calculating relative errors of the action postures of the two sides of the human body according to the action postures of the two sides of the human body and comparing the relative errors with corresponding relative error thresholds;
and the output module is used for outputting a prompt message if the relative error is greater than the relative error threshold, wherein the prompt message is used for prompting a user to correct the current error action posture.
An anaerobic exercise device comprising: a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program:
acquiring movement tracks of two sides of anaerobic exercise equipment when a user uses the anaerobic exercise equipment to build a body; wherein the motion trail is obtained from three-dimensional data of each position in the motion process of the anaerobic motion equipment;
inputting the motion tracks of the two sides of the anaerobic motion equipment into a preset neural network model to obtain the action postures of the two sides of the human body, wherein the preset neural network model comprises the corresponding relation between the motion tracks of the two sides of the human body and the action postures of the two sides of the human body;
calculating relative errors of the action postures of the two sides of the human body according to the action postures of the two sides of the human body, and comparing the relative errors with corresponding relative error thresholds;
and if the relative error is larger than the relative error threshold, outputting a prompt message, wherein the prompt message is used for prompting the user to correct the current error action posture.
A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring movement tracks of two sides of anaerobic exercise equipment when a user uses the anaerobic exercise equipment to build a body; wherein the motion trail is obtained from three-dimensional data of each position in the motion process of the anaerobic motion equipment;
inputting the motion tracks of the two sides of the anaerobic motion equipment into a preset neural network model to obtain the action postures of the two sides of the human body, wherein the preset neural network model comprises the corresponding relation between the motion tracks of the two sides of the human body and the action postures of the two sides of the human body;
calculating relative errors of the action postures of the two sides of the human body according to the action postures of the two sides of the human body, and comparing the relative errors with corresponding relative error thresholds;
and if the relative error is larger than the relative error threshold, outputting a prompt message, wherein the prompt message is used for prompting the user to correct the current error action posture.
The method comprises the steps of firstly obtaining motion tracks of two sides of the anaerobic motion equipment in the motion process, wherein the motion tracks are obtained by calculating three-dimensional data of all positions collected when the anaerobic motion equipment moves to all positions, then obtaining action postures of two sides of a human body by using a preset neural network model according to the motion tracks of the two sides of the anaerobic motion equipment, calculating relative errors of the action postures of the two sides of the human body, finally comparing the relative errors with a preset relative error threshold value, and outputting a prompt message to a user when the relative errors are larger than the relative error threshold value to prompt the user to correct the current wrong action posture. In this embodiment, since the motion trajectory obtained by the anaerobic motion device is obtained based on three-dimensional data, the data transmitted in this embodiment is more diverse. Furthermore, the method of the embodiment can obtain the movement times of the user, and can also obtain the action postures of two sides of the human body according to the neural network model and the movement tracks of two sides of the anaerobic movement equipment, so that the data types obtained by the method of the embodiment are richer. Furthermore, the method of the embodiment can also calculate the relative errors of the action postures of the two sides of the human body and prompt the user to correct the current wrong action posture when the relative errors are large, so that the method of the embodiment can be used for enabling the user to know whether the current action posture of the user is correct or not in time and correcting the current action posture when the current action posture is incorrect, interaction between the user and the anaerobic exercise equipment is enabled to be more intelligent, and user experience is improved.
Drawings
FIG. 1 is a schematic diagram of an exemplary embodiment of an application environment of a method for anaerobic fitness recommendation;
FIG. 2 is a schematic flow chart of a method for oxygen-free exercise fitness recommendation according to an embodiment;
FIG. 3 is a schematic flow chart of a method for oxygen free exercise fitness recommendation according to an embodiment;
FIG. 4 is a schematic flow chart of a method for oxygen free exercise fitness recommendation according to an embodiment;
FIG. 5 is a schematic flow chart of a method for oxygen free exercise fitness recommendation according to an embodiment;
FIG. 6 is a schematic flow chart of a method for oxygen-free exercise fitness recommendation according to another embodiment;
FIG. 7 is a schematic flow chart of a method for oxygen-free exercise fitness recommendation according to another embodiment;
FIG. 8 is a schematic flow diagram of an anaerobic exercise fitness recommendation device according to one embodiment;
FIG. 9 is a schematic flow diagram of an anaerobic exercise fitness recommendation device according to one embodiment;
FIG. 10 is a schematic flow diagram of an anaerobic exercise fitness recommendation device according to another embodiment;
fig. 11 is a schematic flow diagram of an anaerobic exercise fitness recommendation device according to another embodiment.
Description of reference numerals:
101: a sensor; 102: a processor;
103: a memory; 104: a storage medium;
105: a wireless transmission device; 106: a voice device;
107: a display device; 108: an input device.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for recommending the anaerobic exercise fitness provided by the embodiment of the invention can be suitable for the anaerobic exercise equipment shown in figure 1. Optionally, the anaerobic exercise equipment in the embodiment of the invention may be a barbell as shown in fig. 1, a dumbbell, a shoulder pusher, or other anaerobic exercise equipment. As shown in fig. 1, the anaerobic exercise device includes a sensor 101, a processor 102, a memory 103, a storage medium 104, a wireless transmission device 105, a voice device 106, and a display device 107. Wherein, the sensor 101 of the anaerobic exercise device is used for collecting the data of the anaerobic exercise device in the exercise process. Optionally, the data may be motion trajectory data, displacement data, etc. of the oxygen-free device during the motion process. Alternatively, the sensor 101 may be a three-dimensional gravity sensor, a six-axis sensor, a nine-axis sensor, or the like. The processor 102 of the anaerobic exercise device is used to provide computing and control capabilities. The memory 103 of the anaerobic exercise device comprises a storage medium 104 and an internal memory. The storage medium stores an operating system and a computer program. The memory provides an environment for the operating system and the computer programs to run in the non-volatile storage medium. The computer program is executed by a processor to implement an anaerobic exercise fitness recommendation method. The wireless transmission device 105 of the anaerobic exercise device is used for transmitting data stored on the anaerobic exercise device to a cloud platform or a terminal device. The voice device 106 of the anaerobic exercise apparatus is used to output voice prompts to the user. The display device 107 of the anaerobic exercise device is used for displaying the exercise data generated by the anaerobic exercise device during the exercise process. Optionally, the above-mentioned anaerobic exercise device may further include an input device 108. Optionally, the input device 108 may be a touch layer covered on the display device, or may be a key, a track ball, or a touch pad arranged on the casing of the anaerobic exercise device, or may be an external keyboard, a touch pad, or a mouse, etc.
It should be noted that the configuration shown in fig. 1 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation on the anaerobic exercise device to which the present application is applied, and a particular anaerobic exercise device may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components.
In the conventional technology, when data transmission is performed on anaerobic exercise equipment, generally, the transmitted data are one-dimensional data, including displacement, ambient temperature, ambient humidity and the like of the equipment, and the number of exercise, amount of exercise, consumption and the like of a body builder can be calculated by analyzing and processing the data. However, the data transmitted by the data analysis method is single, and the interaction between the user and the anaerobic exercise equipment is not intelligent enough. The embodiment of the invention provides an anaerobic exercise fitness recommendation method and device, anaerobic exercise equipment and a storage medium, and aims to solve the technical problems in the prior art.
It should be noted that the execution subject of the method embodiments described below may be an anaerobic exercise recommendation device, which may be implemented by software, hardware, or a combination of software and hardware as part or all of the above-mentioned anaerobic exercise equipment. The method embodiments described below are described by way of example in which the execution subject is an anaerobic exercise device.
Fig. 2 is a schematic flow chart of a method for recommending anaerobic exercise fitness according to an embodiment. The embodiment relates to a specific process for prompting a user to correct wrong action postures when relative errors are large by using anaerobic exercise equipment to obtain the relative errors of the action postures of two sides of a human body according to motion tracks of the two sides of the anaerobic exercise equipment during body building. As shown in fig. 2, the method may include the steps of:
s101, acquiring movement tracks of two sides of anaerobic exercise equipment when a user uses the anaerobic exercise equipment to build a body; wherein, the motion trail is obtained by three-dimensional data of each position in the motion process of the anaerobic motion equipment.
In this step, the anaerobic exercise device refers to an anaerobic exercise device having two sides, and may be, for example, a dumbbell, a barbell, a bench press, a weight jack, or the like.
Specifically, sensors can be arranged in the two sides of the anaerobic exercise equipment, and the motion tracks generated by the two sides of the anaerobic exercise equipment respectively can be measured by the sensors on the two sides when the user uses the anaerobic exercise equipment to exercise. Wherein, the movement tracks of the two sides of the anaerobic movement equipment are the movement tracks of the two sides of the human body of the user. Optionally, the sensor may be any one of a gyroscope, a three-dimensional gravity sensor, a six-axis sensor, a nine-axis sensor, and an ultrasonic sensor, or may be a sensor in which a plurality of sensors are combined, which is not limited in this embodiment.
Then, the data measured by the sensors are three-dimensional data of each position when the anaerobic exercise equipment moves to each position, and the movement track of the anaerobic exercise equipment can be calculated by using the three-dimensional data of each position. Optionally, the three-dimensional data may be an angular velocity, an angular acceleration, a gravitational acceleration, and the like of the anaerobic exercise device during the exercise process. Optionally, the six-axis sensor and the nine-axis sensor may be used to measure and obtain three-dimensional data such as angular velocity and angular acceleration of the anaerobic exercise device in the three-dimensional direction during the operation process, the three-dimensional gravity sensor may be used to measure and obtain the gravitational acceleration of the anaerobic exercise device in the three-dimensional direction during the operation process, and the three gyroscopes or the three ultrasonic sensors may be respectively arranged in three dimensions to measure and obtain the three-dimensional data.
In addition, besides the three-dimensional data, each sensor can also measure one-dimensional data such as displacement, ambient temperature, ambient humidity and the like of the anaerobic exercise equipment of the user during body building, and the exercise frequency, the exercise amount, the consumption and the like of the user can be calculated according to the data.
S102, inputting the movement tracks of the two sides of the anaerobic movement equipment into a preset neural network model to obtain the action postures of the two sides of the human body, wherein the preset neural network model comprises the corresponding relation between the movement tracks of the two sides of the human body and the action postures of the two sides of the human body.
In this step, optionally, the anaerobic exercise device may use a plurality of training motion trajectory groups as inputs of an initial neural network model, use training motion posture groups corresponding to the plurality of training motion trajectory groups as outputs, and train the initial neural network model to obtain the neural network model; the training motion track group comprises training motion tracks on two sides of the anaerobic motion equipment, and the training action posture group comprises training action postures on two sides of a human body.
Specifically, after obtaining the movement tracks of the two sides of the human body, the anaerobic movement device may input the movement tracks of the two sides into a preset neural network model for matching, where the movement tracks of the two sides of the human body and the movement postures of the two sides of the human body in the neural network model are in one-to-one correspondence, and according to the correspondence, the anaerobic movement device may obtain the movement postures of the two sides of the human body matching the movement tracks of the two sides of the human body. Alternatively, the above-obtained motion postures of the two sides of the human body may be two posture matrixes.
S103, calculating relative errors of the motion postures of the two sides of the human body according to the motion postures of the two sides of the human body, and comparing the relative errors with corresponding relative error thresholds; and if the relative error is larger than the relative error threshold, outputting a prompt message, wherein the prompt message is used for prompting the user to correct the current error action posture.
Specifically, after obtaining the movement postures of the two sides of the human body, the anaerobic exercise device may set the movement posture of one side as a reference posture, and set the movement posture of the other side as a difference value from the reference posture, where the difference value is an error of the movement postures of the two sides of the human body, and the relative error of the movement postures of the two sides of the human body may be obtained by using a quotient of the error and the reference posture.
Further, the anaerobic exercise device may include different action postures of two sides of the human body and corresponding relations between the relative error thresholds thereof, and after obtaining the relative errors of the action postures of the two sides of the human body, the anaerobic exercise device may first find the relative error thresholds corresponding to the action postures of the two sides of the human body according to the corresponding relations, and then compare the obtained relative errors with the corresponding relative error thresholds to obtain a comparison result.
In a possible implementation manner, if the relative error is greater than the relative error threshold, a prompt message needs to be output, optionally, a speaker and an audio decoding device may be disposed in the anaerobic exercise device, and when the prompt message needs to be output, the anaerobic exercise device may decode the prompt message through the audio decoding device, convert the prompt message into a prompt message that can be received by the user, and output the prompt message to the user through the speaker, so that the user can hear the prompt message. Optionally, the content of the prompt message may only indicate that the user has an error in the action posture, and the subsequent specific correction process may be determined by the user himself, or may indicate that the user has an error in the action posture first, and then propose a specific correction process for the user. In another possible implementation manner, if the relative error is less than or equal to the relative error threshold, the anaerobic exercise device may only record the motion tracks of the two sides of the human body and the corresponding motion postures of the two sides of the human body, and does not perform voice prompt.
The method for recommending the anaerobic exercise fitness provided by the embodiment comprises the steps of firstly obtaining the motion tracks of two sides of anaerobic exercise equipment in the motion process, wherein the motion tracks are obtained by calculating three-dimensional data of all points collected when the anaerobic exercise equipment moves to all points, then obtaining action postures of two sides of a human body by using a preset neural network model according to the motion tracks of the two sides of the anaerobic exercise equipment, calculating the relative error of the action postures of the two sides of the human body, finally comparing the relative error with a preset relative error threshold, and outputting a prompt message to a user when the relative error is larger than the relative error threshold to prompt the user to correct the nonstandard action postures. In the embodiment, since the motion trail obtained by the anaerobic motion device is obtained based on three-dimensional data, the data transmitted by the embodiment is more diversified. Furthermore, the method of the embodiment can obtain the movement times of the user, and can also obtain the action postures of two sides of the human body according to the neural network model and the movement tracks of two sides of the anaerobic movement equipment, so that the data obtained by the method of the embodiment is richer. Furthermore, the method of the embodiment can also calculate the relative error of the motion postures of the two sides of the human body, and prompt the user to correct the current wrong motion posture when the relative error is large, so that the method of the embodiment can be used for enabling the user to know whether the current motion posture is correct or not in time, and can correct the current motion posture when the current motion posture is incorrect, thereby improving the interactive experience between the user and the anaerobic exercise equipment.
Fig. 3 is a schematic flow chart of a method for recommending anaerobic exercise fitness according to an embodiment. The embodiment relates to a specific process of how the anaerobic exercise equipment acquires the motion tracks of two sides of the anaerobic exercise equipment when a user uses the anaerobic exercise equipment for body building. As shown in fig. 3, the step S101 may include the following steps:
s201, in the movement process of the anaerobic movement equipment, acquiring the angular acceleration, the angular velocity and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position.
Specifically, the movement track of the anaerobic exercise device is composed of all positions in the movement process, and the sensor is utilized, so that the anaerobic exercise device can measure and obtain the angular acceleration, the angular velocity and the displacement when the anaerobic exercise device moves to all points. The three dimensions refer to X, Y, Z axes, and the sensors measure X, Y, Z angular acceleration, angular velocity and displacement of the anaerobic exercise equipment in three axial directions.
S202, calculating the angle of the anaerobic motion device in the three-dimensional direction when the anaerobic motion device moves to each position by utilizing a preset angle conversion algorithm according to the angular acceleration and the angular velocity of the anaerobic motion device in the three-dimensional direction when the anaerobic motion device moves to each position.
Specifically, because a plurality of angular accelerations and a plurality of angular velocities measured by the sensor have a certain measurement error, before calculating the motion trajectories on both sides of the anaerobic exercise device, the angular accelerations and the angular velocities with errors can be filtered by using a kalman filter algorithm, so that the optimal angular accelerations and the optimal angular velocities when the anaerobic exercise device moves to various positions are obtained. And then the anaerobic movement equipment can calculate the optimal angular acceleration and the optimal angular velocity through an angular conversion algorithm such as a rotation matrix, quaternion attitude calculation, Euler angle calculation and the like, so that the angle of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position is obtained.
S203, obtaining the movement tracks of the two sides of the anaerobic movement equipment according to the angle and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position.
Specifically, the anaerobic exercise device can determine coordinates of the anaerobic exercise device when the anaerobic exercise device moves to each position according to the obtained plurality of angles and the obtained plurality of displacements, and then the anaerobic exercise device can perform curve fitting on the plurality of coordinates to obtain the movement track of the anaerobic exercise device.
According to the anaerobic exercise fitness recommendation method provided by the embodiment, angular accelerations and angular velocities of X, Y, Z pieces of anaerobic exercise equipment in three axial directions are measured, a plurality of angles of the anaerobic exercise equipment in the three-dimensional directions when the anaerobic exercise equipment moves to various positions are calculated according to the plurality of angular accelerations and the plurality of angular velocities in the three-dimensional directions obtained through the measurement, and finally the movement track of the anaerobic exercise equipment is fitted according to the plurality of angles and displacements in the three-dimensional directions, so that data transmitted by the embodiment are more diverse, and meanwhile, the movement track of the anaerobic exercise equipment obtained through the embodiment is more accurate.
Fig. 4 is a flowchart illustrating a method for recommending anaerobic exercise fitness according to an embodiment. The embodiment relates to a specific process of obtaining the action postures of two sides of a human body by anaerobic exercise equipment according to the motion tracks of the two sides of the anaerobic exercise equipment. As shown in fig. 4, the step S102 may include the following steps:
s301, recognizing the types of the motion tracks on the two sides of the anaerobic motion equipment by adopting the neural network model.
The neural network model can also comprise a plurality of corresponding relations between the motion tracks of the two sides of the human body and a plurality of categories. For example, the categories may be push forward, pull backward, and the like.
Specifically, after the anaerobic exercise device inputs the movement tracks of the two sides of the human body into the preset neural network model, the categories corresponding to the movement tracks of the two sides of the human body can be obtained according to the corresponding relationship. By the type identification, which type of action the human body specifically does can be determined.
S302, obtaining the action postures of the two sides of the human body according to the corresponding relation between the types of the motion tracks of the two sides of the anaerobic motion equipment and the action postures of the two sides of the human body in the neural network model.
Specifically, the neural network model may also include a correspondence between the types of the motion trajectories on both sides of the human body and the motion postures on both sides of the human body, and the anaerobic exercise device may determine the motion postures on both sides of the human body corresponding to the motion trajectories on both sides of the human body according to the correspondence.
According to the anaerobic exercise fitness recommendation method provided by the embodiment, the types of the motion tracks on the two sides of the anaerobic exercise equipment are firstly identified, and then the action postures on the two sides of the human body are determined according to the types, so that the obtained action postures on the two sides of the human body are accurate, and a guarantee is provided for the subsequent calculation of the relative errors of the action postures on the two sides of the human body.
Fig. 5 is a flowchart illustrating a method for recommending anaerobic exercise fitness according to an embodiment. The embodiment relates to a specific process of how the anaerobic exercise device calculates the relative error of the action postures of the two sides of the human body. As shown in fig. 5, the calculating of the relative error of the motion postures of the two sides of the human body in the above step S103 may include the steps of:
s401, calculating relative errors of the motion tracks of the two sides of the human body.
Specifically, after obtaining the movement tracks of the two sides of the human body, the anaerobic movement device can optionally directly calculate the relative error of the two movement tracks, or selectively map the two movement tracks to the same axis, and then calculate the relative error of the two movement tracks mapped on the same axis. Optionally, the calculating of the relative error of the two motion tracks may be to calculate the relative error of peaks of the two motion tracks, or to calculate the relative error of troughs of the two motion tracks, which is not limited in this embodiment.
S402, determining the relative error of the motion tracks of the two sides of the human body as the relative error of the motion postures of the two sides of the human body.
According to the anaerobic exercise fitness recommendation method provided by the embodiment, the relative errors of the motion tracks of the two sides of the human body are calculated, and finally the relative errors of the motion tracks of the two sides of the human body can be determined as the relative errors of the action postures of the two sides of the human body.
Fig. 6 is a schematic flow chart of a method for recommending anaerobic exercise fitness according to another embodiment. The embodiment relates to a specific process of generating personalized fitness recommendation information for a user by anaerobic exercise equipment according to information input by the user and temperature and humidity information of an environment. As shown in fig. 6, the method may include the steps of:
s501, acquiring user information and temperature and humidity information of an environment, wherein the user information comprises at least one of the following items: user's preferred exercise type, age, joint size, past medical history, heart rate, blood oxygen concentration, body temperature, historical exercise data.
The user information comprises user physiological parameters, and the user physiological parameters comprise heart rate, blood oxygen concentration, body temperature and the like of the user.
In this step, optionally, the anaerobic exercise device itself may have a display screen, and the user may select related information on the display screen, or the anaerobic exercise device may include a wireless transmission module inside, and the wireless transmission module may perform data transmission with an APP (Application, device Application), and after the user selects the related information on the APP, the APP may transmit the related information selected by the user to the anaerobic exercise device through the wireless transmission module.
In addition, when the anaerobic exercise device collects temperature and humidity information of the environment, the temperature of the environment can be collected through a built-in temperature sensor and the humidity of the environment can be collected through another built-in humidity sensor, the temperature and the humidity of the environment can also be collected through the built-in temperature and humidity sensor of the anaerobic exercise device, and the embodiment does not limit the temperature and the humidity information. Alternatively, the temperature sensor may be a DS18B20 temperature sensor, an infrared temperature detection sensor, a thermistor, or the like, the humidity sensor may be a humidity sensing element, a humidity sensor, an SHT20 humidity sensor, or the like, and the temperature/humidity sensor may be a DHT11 temperature/humidity sensor, or the like.
And S502, determining a fitness recommendation scheme matched with the user information and the temperature and humidity information of the environment.
Specifically, the anaerobic exercise device can input the user information and the environmental temperature and humidity information into a preset fitness scheme generation model, the preset fitness scheme generation model comprises a plurality of user information and corresponding relations between the environmental temperature and humidity information and a plurality of fitness recommendation schemes, and according to the corresponding relations, the anaerobic exercise device can obtain the fitness recommendation schemes corresponding to the user information and the environmental temperature and humidity information. By utilizing the fitness recommendation scheme, the user can perform fitness in a targeted manner.
According to the anaerobic exercise fitness recommendation method provided by the embodiment, the fitness recommendation scheme belonging to the individual user is generated according to the user information and the temperature and humidity information of the environment, so that the method can be used for the user to perform fitness specifically, the fitness of the user can be more scientific, and the fitness interest of the user can be improved.
Fig. 7 is a schematic flow chart of a method for recommending anaerobic exercise fitness according to another embodiment. The embodiment relates to a specific process of judging whether the current physiological parameter is matched with the standard physiological parameter or not by the anaerobic exercise equipment according to the current physiological parameter of the user and the selected fitness scheme, and outputting an early warning message to the user when the current physiological parameter is not matched with the standard physiological parameter. As shown in fig. 7, the method may include the steps of:
s601, acquiring the current physiological parameters of the user and the selected fitness scheme, wherein the physiological parameters comprise at least one of heart rate, blood oxygen concentration and body temperature, and the fitness scheme comprises standard physiological parameters.
Wherein, anaerobic exercise equipment can embed heart rate detection sensor for gather user's heart rate, also can embed blood oxygen concentration sensor, be used for gathering user's blood oxygen concentration, can also embed temperature detection sensor for detect user's body temperature. Optionally, the temperature detection sensor for detecting the body temperature of the user may be the same temperature sensor as the temperature sensor for measuring the ambient temperature, or may be two temperature sensors for respectively detecting the body temperature of the user and measuring the ambient temperature, which is not limited in this embodiment.
Specifically, the anaerobic exercise device may include a plurality of exercise protocols, and when a user needs to exercise, one of the exercise protocols may be selected, where the exercise protocol includes a standard physiological parameter, and the standard physiological parameter refers to a physiological parameter used when the exercise protocol is generated, and optionally, the standard physiological parameter may be a certain value, or may be a range, where the range refers to a range formed by a maximum value and a minimum value of the physiological parameter used when the exercise protocol is generated.
S602, judging whether the current physiological parameters of the user are matched with the standard physiological parameters in the fitness scheme; and if not, outputting an early warning message, wherein the early warning message is used for instructing the user to modify the current fitness scheme.
Specifically, after obtaining the current physiological parameters and the standard physiological parameters of the user, the anaerobic exercise device may match the current physiological parameters with the standard physiological parameters, and in a possible implementation, if the matching is successful, the user may use the fitness scheme to perform fitness; it should be noted that, if there are a plurality of acquired physiological parameters of the user, it is necessary that all of the plurality of physiological parameters are successfully matched with the standard physiological parameters, and then the matching can be successful. In another possible embodiment, if the match is not successful, the user cannot use the current exercise regimen to exercise, requiring a re-selection of the exercise regimen. Optionally, when the anaerobic exercise device outputs the early warning message to the user, a structure combining a buzzer and a triode may be adopted, the triode is used for amplifying the early warning message, and the buzzer is used for outputting the early warning message amplified by the triode.
According to the anaerobic exercise fitness recommendation method provided by the embodiment, the current physiological parameters of the user are matched with the standard physiological parameters in the fitness scheme selected by the user, so that the user can use the selected fitness scheme to perform fitness when the matching is successful, and the user preferably does not select the fitness scheme to perform fitness when the matching is unsuccessful. Therefore, the problem of sports injury caused by the fact that the user blindly selects a fitness scheme which is not suitable for the user can be avoided to a certain extent.
It should be understood that although the various steps in the flow charts of fig. 2-7 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-7 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
Fig. 8 is a schematic structural diagram of an anaerobic exercise fitness recommendation device according to an embodiment. As shown in fig. 8, the apparatus may include: the device comprises a track acquisition module 10, a determination module 11, a calculation module 12 and an output module 13.
Specifically, the trajectory acquisition module 10 is configured to acquire a motion trajectory of two sides of the anaerobic exercise device when a user uses the anaerobic exercise device for body-building; wherein the motion trail is obtained from three-dimensional data of each position in the motion process of the anaerobic motion equipment;
the determining module 11 is configured to input the motion trajectories of the two sides of the anaerobic motion device into a preset neural network model to obtain the action postures of the two sides of the human body, where the preset neural network model includes a correspondence between the motion trajectories of the two sides of the human body and the action postures of the two sides of the human body;
the calculation module 12 is configured to calculate relative errors of the motion postures of the two sides of the human body according to the motion postures of the two sides of the human body, and compare the relative errors with corresponding relative error thresholds;
an output module 13, configured to output a prompt message if the relative error is greater than the relative error threshold, where the prompt message is used to prompt a user to correct a current incorrect action posture.
The anaerobic exercise fitness recommendation device provided by the embodiment can execute the method embodiment, the implementation principle and the technical effect are similar, and the detailed description is omitted.
In an embodiment, on the basis of the above embodiment, the trajectory acquisition module 10 is specifically configured to acquire, during the movement of the anaerobic movement device, an angular acceleration, an angular velocity, and a displacement of the anaerobic movement device in a three-dimensional direction when the anaerobic movement device moves to each position; calculating the angle of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position by utilizing a preset angle conversion algorithm according to the angular acceleration and the angular velocity of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position; and obtaining the movement tracks of the two sides of the anaerobic movement equipment according to the angle and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position.
In an embodiment, on the basis of the above embodiment, the determining module 11 is specifically configured to identify the categories of the motion trajectories on both sides of the anaerobic motion device by using the neural network model; and obtaining the action postures of the two sides of the human body according to the corresponding relation between the types of the motion tracks of the two sides of the anaerobic motion equipment and the action postures of the two sides of the human body in the neural network model.
In an embodiment, on the basis of the above embodiment, the calculating module 12 is specifically configured to calculate relative errors of the motion trajectories of two sides of the human body; and determining the relative error of the motion tracks of the two sides of the human body as the relative error of the action postures of the two sides of the human body.
Fig. 9 is a schematic structural diagram of an anaerobic exercise fitness recommendation device according to an embodiment. As shown in fig. 9, on the basis of the above embodiment, the apparatus may further include: a model training module 14.
Specifically, the model training module 14 is configured to train the initial neural network model by using a plurality of training motion trajectory groups as inputs of the initial neural network model and using training motion posture groups corresponding to the plurality of training motion trajectory groups as outputs, so as to obtain the neural network model; the training motion track group comprises training motion tracks on two sides of the anaerobic motion equipment, and the training action posture group comprises training action postures on two sides of a human body.
Fig. 10 is a schematic structural view of an anaerobic exercise fitness recommendation device according to another embodiment. As shown in fig. 10, on the basis of the above embodiment, the apparatus may further include: an information acquisition module 15 and a scheme determination module 16.
Specifically, the information obtaining module 15 is configured to obtain user information and temperature and humidity information of an environment, where the user information includes at least one of the following: a user's preferred exercise type, age, joint size, past medical history, heart rate, blood oxygen concentration, body temperature, historical exercise data;
and the scheme determining module 16 is configured to determine a fitness recommendation scheme matched with the user information and the temperature and humidity information of the environment.
Fig. 11 is a schematic structural view of an anaerobic exercise fitness recommendation device according to another embodiment. As shown in fig. 11, on the basis of the above embodiment, the apparatus may further include: the device comprises a comprehensive acquisition module 17, a judgment module 18 and an early warning output module 19.
Specifically, the comprehensive acquisition module 17 is configured to acquire a current physiological parameter of the user and a selected fitness scheme, where the physiological parameter includes at least one of heart rate, blood oxygen concentration, and body temperature, and the fitness scheme includes a standard physiological parameter;
a judging module 18, configured to judge whether the current physiological parameter of the user matches a standard physiological parameter in the fitness plan;
and the early warning output module 19 is used for outputting an early warning message if the user does not match the current fitness scheme, and the early warning message is used for instructing the user to modify the current fitness scheme.
The anaerobic exercise fitness recommendation device provided by the embodiment can execute the method embodiment, the implementation principle and the technical effect are similar, and the detailed description is omitted.
In one embodiment, there is provided an anaerobic exercise device comprising: a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program:
acquiring movement tracks of two sides of anaerobic exercise equipment when a user uses the anaerobic exercise equipment to build a body; wherein the motion trail is obtained from three-dimensional data of each position in the motion process of the anaerobic motion equipment;
inputting the motion tracks of the two sides of the anaerobic motion equipment into a preset neural network model to obtain the action postures of the two sides of the human body, wherein the preset neural network model comprises the corresponding relation between the motion tracks of the two sides of the human body and the action postures of the two sides of the human body;
calculating relative errors of the action postures of the two sides of the human body according to the action postures of the two sides of the human body, and comparing the relative errors with corresponding relative error thresholds;
and if the relative error is larger than the relative error threshold, outputting a prompt message, wherein the prompt message is used for prompting the user to correct the current error action posture.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
in the movement process of the anaerobic movement equipment, acquiring the angular acceleration, the angular velocity and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position;
calculating the angle of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position by utilizing a preset angle conversion algorithm according to the angular acceleration and the angular velocity of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position;
and obtaining the movement tracks of the two sides of the anaerobic movement equipment according to the angle and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
identifying the types of the motion tracks on the two sides of the anaerobic motion equipment by adopting the neural network model;
and obtaining the action postures of the two sides of the human body according to the corresponding relation between the types of the motion tracks of the two sides of the anaerobic motion equipment and the action postures of the two sides of the human body in the neural network model.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
calculating relative errors of the motion tracks of the two sides of the human body;
and determining the relative error of the motion tracks of the two sides of the human body as the relative error of the action postures of the two sides of the human body.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
taking a plurality of training motion trajectory groups as input of an initial neural network model, taking training action posture groups corresponding to the training motion trajectory groups as output, and training the initial neural network model to obtain the neural network model;
the training motion track group comprises training motion tracks on two sides of the anaerobic motion equipment, and the training action posture group comprises training action postures on two sides of a human body.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring user information and temperature and humidity information of an environment, wherein the user information comprises at least one of the following items: a user's preferred exercise type, age, joint size, past medical history, heart rate, blood oxygen concentration, body temperature, historical exercise data;
and determining a fitness recommendation scheme matched with the user information and the temperature and humidity information of the environment.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring current physiological parameters of a user and a selected fitness scheme, wherein the physiological parameters comprise at least one of heart rate, blood oxygen concentration and body temperature, and the fitness scheme comprises standard physiological parameters;
judging whether the current physiological parameters of the user are matched with the standard physiological parameters in the fitness scheme;
and if not, outputting an early warning message, wherein the early warning message is used for instructing the user to modify the current fitness scheme.
In one embodiment, a readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring movement tracks of two sides of anaerobic exercise equipment when a user uses the anaerobic exercise equipment to build a body; wherein the motion trail is obtained from three-dimensional data of each position in the motion process of the anaerobic motion equipment;
inputting the motion tracks of the two sides of the anaerobic motion equipment into a preset neural network model to obtain the action postures of the two sides of the human body, wherein the preset neural network model comprises the corresponding relation between the motion tracks of the two sides of the human body and the action postures of the two sides of the human body;
calculating relative errors of the action postures of the two sides of the human body according to the action postures of the two sides of the human body, and comparing the relative errors with corresponding relative error thresholds;
and if the relative error is larger than the relative error threshold, outputting a prompt message, wherein the prompt message is used for prompting the user to correct the current error action posture.
In one embodiment, the computer program when executed by the processor further performs the steps of:
in the movement process of the anaerobic movement equipment, acquiring the angular acceleration, the angular velocity and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position;
calculating the angle of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position by utilizing a preset angle conversion algorithm according to the angular acceleration and the angular velocity of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position;
and obtaining the movement tracks of the two sides of the anaerobic movement equipment according to the angle and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position.
In one embodiment, the computer program when executed by the processor further performs the steps of:
identifying the types of the motion tracks on the two sides of the anaerobic motion equipment by adopting the neural network model;
and obtaining the action postures of the two sides of the human body according to the corresponding relation between the types of the motion tracks of the two sides of the anaerobic motion equipment and the action postures of the two sides of the human body in the neural network model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
calculating relative errors of the motion tracks of the two sides of the human body;
and determining the relative error of the motion tracks of the two sides of the human body as the relative error of the action postures of the two sides of the human body.
In one embodiment, the computer program when executed by the processor further performs the steps of:
taking a plurality of training motion trajectory groups as input of an initial neural network model, taking training action posture groups corresponding to the training motion trajectory groups as output, and training the initial neural network model to obtain the neural network model;
the training motion track group comprises training motion tracks on two sides of the anaerobic motion equipment, and the training action posture group comprises training action postures on two sides of a human body.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring user information and temperature and humidity information of an environment, wherein the user information comprises at least one of the following items: a user's preferred exercise type, age, joint size, past medical history, heart rate, blood oxygen concentration, body temperature, historical exercise data;
and determining a fitness recommendation scheme matched with the user information and the temperature and humidity information of the environment.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring current physiological parameters of a user and a selected fitness scheme, wherein the physiological parameters comprise at least one of heart rate, blood oxygen concentration and body temperature, and the fitness scheme comprises standard physiological parameters;
judging whether the current physiological parameters of the user are matched with the standard physiological parameters in the fitness scheme;
and if not, outputting an early warning message, wherein the early warning message is used for instructing the user to modify the current fitness scheme.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An anaerobic exercise fitness recommendation method, the method comprising:
acquiring movement tracks of two sides of anaerobic exercise equipment when a user uses the anaerobic exercise equipment to build a body; wherein the motion trail is obtained from three-dimensional data of each position in the motion process of the anaerobic motion equipment;
inputting the motion tracks of the two sides of the anaerobic motion equipment into a preset neural network model to obtain the action postures of the two sides of the human body, wherein the preset neural network model comprises the corresponding relation between the motion tracks of the two sides of the human body and the action postures of the two sides of the human body;
calculating relative errors of the action postures of the two sides of the human body according to the action postures of the two sides of the human body, and comparing the relative errors with corresponding relative error thresholds; the relative error is determined according to a quotient of a difference value and a reference posture after the action posture of one side is set as the reference posture and the action posture of the other side is different from the reference posture;
and if the relative error is larger than the relative error threshold, outputting a prompt message, wherein the prompt message is used for prompting the user to correct the current error action posture.
2. The method of claim 1, wherein the obtaining of the motion trajectory of the two sides of the anaerobic exercise device while the user exercises with the anaerobic exercise device comprises:
in the movement process of the anaerobic movement equipment, acquiring the angular acceleration, the angular velocity and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position;
calculating the angle of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position by utilizing a preset angle conversion algorithm according to the angular acceleration and the angular velocity of the anaerobic exercise equipment in the three-dimensional direction when the anaerobic exercise equipment moves to each position;
and obtaining the movement tracks of the two sides of the anaerobic movement equipment according to the angle and the displacement of the anaerobic movement equipment in the three-dimensional direction when the anaerobic movement equipment moves to each position.
3. The method of claim 1, wherein the inputting the motion trajectories of the two sides of the anaerobic motion device into a preset neural network model to obtain the motion postures of the two sides of the human body comprises:
identifying the types of the motion tracks on the two sides of the anaerobic motion equipment by adopting the neural network model;
and obtaining the action postures of the two sides of the human body according to the corresponding relation between the types of the motion tracks of the two sides of the anaerobic motion equipment and the action postures of the two sides of the human body in the neural network model.
4. The method of claim 1, wherein the calculating the relative error of the motion posture of the two sides of the human body comprises:
calculating relative errors of the motion tracks of the two sides of the human body;
and determining the relative error of the motion tracks of the two sides of the human body as the relative error of the action postures of the two sides of the human body.
5. The method according to any one of claims 1-4, further comprising:
taking a plurality of training motion trajectory groups as input of an initial neural network model, taking training action posture groups corresponding to the training motion trajectory groups as output, and training the initial neural network model to obtain the neural network model;
the training motion track group comprises training motion tracks on two sides of the anaerobic motion equipment, and the training action posture group comprises training action postures on two sides of a human body.
6. The method of claim 1, further comprising:
acquiring user information and temperature and humidity information of an environment, wherein the user information comprises at least one of the following items: a user's preferred exercise type, age, joint size, past medical history, heart rate, blood oxygen concentration, body temperature, historical exercise data;
and determining a fitness recommendation scheme matched with the user information and the temperature and humidity information of the environment.
7. The method of claim 1, further comprising:
acquiring current physiological parameters of a user and a selected fitness scheme, wherein the physiological parameters comprise at least one of heart rate, blood oxygen concentration and body temperature, and the fitness scheme comprises standard physiological parameters;
judging whether the current physiological parameters of the user are matched with the standard physiological parameters in the fitness scheme;
and if not, outputting an early warning message, wherein the early warning message is used for instructing the user to modify the current fitness scheme.
8. An anaerobic exercise fitness recommendation device, the device comprising:
the trajectory acquisition module is used for acquiring the movement trajectories of two sides of the anaerobic exercise equipment when a user uses the anaerobic exercise equipment for body building; wherein the motion trail is obtained from three-dimensional data of each position in the motion process of the anaerobic motion equipment;
the determining module is used for inputting the motion tracks of the two sides of the anaerobic motion equipment into a preset neural network model to obtain the action postures of the two sides of the human body, and the preset neural network model comprises the corresponding relation between the motion tracks of the two sides of the human body and the action postures of the two sides of the human body;
the calculation module is used for calculating relative errors of the action postures of the two sides of the human body according to the action postures of the two sides of the human body and comparing the relative errors with corresponding relative error thresholds; the relative error is determined according to a quotient of a difference value and a reference posture after the action posture of one side is set as the reference posture and the action posture of the other side is different from the reference posture;
and the output module is used for outputting a prompt message if the relative error is greater than the relative error threshold, wherein the prompt message is used for prompting a user to correct the current error action posture.
9. An anaerobic exercise device comprising: memory storing a computer program, and a processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.
10. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910035532.1A CN109830278B (en) | 2019-01-15 | 2019-01-15 | Anaerobic exercise fitness recommendation method and device, anaerobic exercise equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910035532.1A CN109830278B (en) | 2019-01-15 | 2019-01-15 | Anaerobic exercise fitness recommendation method and device, anaerobic exercise equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109830278A CN109830278A (en) | 2019-05-31 |
| CN109830278B true CN109830278B (en) | 2021-01-15 |
Family
ID=66861602
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910035532.1A Active CN109830278B (en) | 2019-01-15 | 2019-01-15 | Anaerobic exercise fitness recommendation method and device, anaerobic exercise equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109830278B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114065059B (en) * | 2022-01-17 | 2022-04-19 | 浙江想能睡眠科技股份有限公司 | Sleep posture recommendation control method and system based on big data and storage medium |
| CN114864042A (en) * | 2022-04-26 | 2022-08-05 | 云知声智能科技股份有限公司 | Method and device for correcting motion, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205287541U (en) * | 2015-12-11 | 2016-06-08 | 乐视体育文化产业发展（北京）有限公司 | Body -building equipment |
| CN106422206A (en) * | 2016-09-08 | 2017-02-22 | 哈尔滨工程大学 | Motion standardization recognition method based on intelligent bracelet |
| CN108766572A (en) * | 2018-05-30 | 2018-11-06 | 努比亚技术有限公司 | Motion monitoring control method, intelligent wearable device and storage medium |
| CN109011508A (en) * | 2018-07-30 | 2018-12-18 | 三星电子（中国）研发中心 | A kind of intelligent coach system and method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10360811B2 (en) * | 2015-01-06 | 2019-07-23 | Asensei, Inc. | Movement based fitness and fitness product management |
| CN105667647A (en) * | 2016-01-15 | 2016-06-15 | 南京理工大学 | Motion state indicating device and implementation method thereof |
| CN105833508A (en) * | 2016-03-18 | 2016-08-10 | 深圳大学 | Intelligent gloves capable of measuring calorie consumption and monitoring hand posture identification as well as estimation method and system |
| CN106874874A (en) * | 2017-02-16 | 2017-06-20 | 南方科技大学 | Motion state identification method and device |
-
2019
- 2019-01-15 CN CN201910035532.1A patent/CN109830278B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205287541U (en) * | 2015-12-11 | 2016-06-08 | 乐视体育文化产业发展（北京）有限公司 | Body -building equipment |
| CN106422206A (en) * | 2016-09-08 | 2017-02-22 | 哈尔滨工程大学 | Motion standardization recognition method based on intelligent bracelet |
| CN108766572A (en) * | 2018-05-30 | 2018-11-06 | 努比亚技术有限公司 | Motion monitoring control method, intelligent wearable device and storage medium |
| CN109011508A (en) * | 2018-07-30 | 2018-12-18 | 三星电子（中国）研发中心 | A kind of intelligent coach system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109830278A (en) | 2019-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10898755B2 (en) | Method for providing posture guide and apparatus thereof | |
| WO2022193851A1 (en) | Method and system for recognizing user actions | |
| CN111477297A (en) | Personal computing device | |
| CN109830278B (en) | Anaerobic exercise fitness recommendation method and device, anaerobic exercise equipment and storage medium | |
| CN110477924B (en) | Adaptive motion attitude sensing system and method | |
| CN108836344B (en) | Step length step frequency estimation method and device and gait detector | |
| AU2021202003B2 (en) | Methods and systems for bicycle fitting | |
| CN109731316B (en) | Shooting training system | |
| US11482046B2 (en) | Action-estimating device | |
| US20240221427A1 (en) | Physical activity measurement and analysis | |
| CN117109567A (en) | Riding gesture monitoring method and system for dynamic bicycle movement and wearable riding gesture monitoring equipment | |
| JP7023004B2 (en) | Motion analysis system, motion analysis program, and motion analysis method | |
| CN114403858B (en) | Human body movement function assessment method, device and system | |
| US10741095B2 (en) | Teaching compatibility determining device, system, method and recording medium | |
| KR20220096279A (en) | Smart mirror-based posture correction system | |
| TWI581765B (en) | Movement-orbit sensing system and movement-orbit collecting method by using the same | |
| CN112753056B (en) | System and method for physical training of body parts | |
| US20160151667A1 (en) | Movement-orbit sensing system and movement-orbit collecting method using the same | |
| KR20110033001A (en) | Training assistance apparatus and method | |
| CN118690233A (en) | Rope skipping action recognition system and method based on deep learning | |
| Lim et al. | uLift: Adaptive Workout Tracker Using a Single Wrist-Worn Accelerometer | |
| CN118196894A (en) | Finger movement vision detection method, system, computer device and storage medium | |
| Song | User Activity Tracker using Android Sensor | |
| CN114797005A (en) | Rehabilitation training method, system and device | |
| CN118593959A (en) | Rope skipping training method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |