CN118179002A - Linkage control method and system for fitness equipment - Google Patents
Linkage control method and system for fitness equipment Download PDFInfo
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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
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
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- A—HUMAN NECESSITIES
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- 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
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
- A63B71/0622—Visual, audio or audio-visual systems for entertaining, instructing or motivating the user
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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
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
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- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
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- G06V40/20—Movements or behaviour, e.g. gesture recognition
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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
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
- A63B71/0622—Visual, audio or audio-visual systems for entertaining, instructing or motivating the user
- A63B2071/0625—Emitting sound, noise or music
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Abstract
The invention relates to the technical field of instrument linkage control, in particular to a body-building instrument linkage control method and system. According to the exercise machine linkage control method and system, the frequencies in the unit time of the exercise action of the user are clustered, the optimal frequency set in the unit time of the exercise of the user is determined, and if the frequency set is counted once, the accuracy of the exercise of the user can be effectively improved, and further the exercise effect is improved.
Description
Technical Field
The invention relates to the technical field of instrument linkage control, in particular to a linkage control method and system for fitness equipment.
Background
Along with the progress of social civilization, people hope to have physical and mental health, prolong life, pursue mental satisfaction and enjoy life pleasure. The sports is one of the important modes for realizing the aim, has the functions of multiple aspects such as fitness, entertainment, ideology and the like, can enrich the cultural life of human beings, improve the cultural level and the quality of life, improve the living habit of people, cultivate the fashion style and has positive regulation effect on the benign development of society. Therefore, going to the gym becomes the first choice method for people to exercise the body.
As indoor exercise becomes more popular, people have seen exercise as a daily item for improving physical conditions. However, the existing fitness equipment and system are large in size, heavy in weight and inconvenient to move, can not form an immersive fitness scene, and can not mutually adjust functions of the fitness equipment.
A data interaction exercise system disclosed in the patent document with publication number CN107158661a, the system includes an intelligent gateway, an intelligent exercise machine, a mobile app, and a pc application system. The intelligent gateway is in connection communication with the intelligent fitness equipment, the mobile terminal app and the pc terminal application system through a wireless network; the intelligent gateway is provided with private cloud, wireless routing and fitness equipment control hub; the intelligent body-building apparatus needs to be provided with a human body sensor, an NFC control chip and a Zi gBee module; the mobile terminal app is configured to obtain fitness and health index information of a user through NFC communication; the pc end application system is used for acquiring user body-building information uploaded to the private cloud by the intelligent gateway through the wireless network, and analyzing and processing data aiming at a user specified health target and the user body-building information. The invention provides the body-building system with high intelligence and strong specialization, which helps people to perform scientific exercise aiming at personal characteristics, and achieves the optimal effect.
However, the data interaction body-building system cannot simulate a large number of kinds of body-building apparatuses by adopting fewer components, cannot construct an immersive body-building scene for a user, and cannot control the accuracy of body-building actions of the user.
Disclosure of Invention
The invention aims to provide a linkage control method and system for fitness equipment, which are used for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the utility model provides a fitness equipment coordinated control method and system, includes data acquisition sensing module, camera module, music broadcast module and control module, data acquisition sensing module includes face identification unit, fingerprint identification unit and motion data sensing unit, face identification unit and fingerprint identification unit establish user's body-building database based on user's identity information, motion data sensing unit establishes user's body-building database after obtaining optimum motion data based on clustering algorithm.
The camera module is used for identifying and detecting gesture information of key points of human bones after preprocessing a shot image or video, and counting the moving gestures after determining the linkage change range of the key points of the bones.
The music playing module plays songs interested by the user in the user body building process after the user listening log is imported into the user body building database.
Preferably, the face recognition unit and the fingerprint recognition unit are used for acquiring identity information of a user and generating corresponding user ID serial numbers, and binding and matching of the user information and the acquired user body-building information in the body-building database are realized through the user ID serial numbers.
Preferably, the face recognition unit and the fingerprint recognition unit comprise face recognition equipment and fingerprint acquisition equipment at the entrance of the gymnasium and on the gymnasium, and the face recognition equipment and the fingerprint acquisition equipment are connected with the terminal equipment through network signals.
Preferably, the motion data sensing unit comprises a counting sensor and a light device, and the counting sensor clusters the frequency of the user body-building action after transmitting the data to the terminal device to determine the optimal frequency set when the user body-building.
Preferably, the control module generates a corresponding control instruction according to the received real-time frequency value of the exercise equipment by judging whether the frequency of the user during exercise is in the optimal frequency aggregate, and controls the real-time color state of the light equipment through the control instruction.
Preferably, the real-time color state of the light equipment is divided into red, green and blue, when the frequency of the user during exercise is lower than the optimal frequency set, the real-time color of the light equipment is controlled to be blue by the instruction sent by the control module, when the frequency of the user during exercise is higher than the optimal frequency set, the real-time color of the light equipment is controlled to be red by the instruction sent by the control module, and when the frequency of the user during exercise is in the optimal frequency set, the real-time color of the light equipment is controlled to be green by the instruction sent by the control module.
Preferably, the key points of the human skeleton refer to 16 specific positions of the head, the shoulder, the knee, the ankle and the like of the human body, and the change range is determined by observing the angle change of the key points in a certain body-building action.
Preferably, after the camera module determines the identity of the user based on the face detector, the camera module transmits the shot image and video to the processor for image preprocessing, detects and positions objects from the processed image or video, recognizes key points and gesture information of human bones through the gesture estimation model by using the detected human body information, observes the change of the key points of the bones according to different body-building actions, and counts the changes by using a counting algorithm.
Preferably, the control module comprises a host terminal of the gymnasium, the host terminal is connected with the face recognition equipment and the fingerprint acquisition equipment in the data acquisition sensing module through network signals, and the host terminal is electrically connected with the counting sensor and the lamplight equipment in the motion data sensing unit.
A linkage control method of fitness equipment comprises the following steps:
S1: when a user enters a gymnasium, face recognition equipment and fingerprint recognition equipment at the entrance of the gymnasium can recognize the user, acquire identity information of the user and generate corresponding user ID serial numbers, a user gymnasium database is built by using the serial numbers, and binding and matching of user information and acquired user gymnasium information in the gymnasium database are realized through the user ID serial numbers;
S2: the user can import own song listening logs into the music playing module of the exercise equipment, and can play music according to own preference in the exercise process;
S3: after the camera module determines the identity of the user based on the face detector, the camera module transmits the shot image and video to the processor for image preprocessing, detects and positions objects from the processed image or video, recognizes key points and gesture information of human bones through the gesture estimation model according to the detected human body information, observes the change of the key points of the bones according to different body-building actions, and counts the changes by using a counting algorithm;
s4: in the user body building process, a counting sensor on the body building equipment calculates the action frequency of the user body building, a control module clusters the frequency in the unit time of the user body building action, an optimal frequency set in the unit time of the user body building is determined, if the optimal frequency set is in the optimal frequency set, the counting is carried out once and is displayed on a display screen of the body building equipment, and otherwise, the counting is not displayed.
Compared with the prior art, the invention has the beneficial effects that:
According to the exercise equipment linkage control method and system, the user exercise database is built after the optimal exercise data are obtained based on the user identity information and the clustering algorithm, after the shot images or videos are preprocessed, objects are detected and positioned from the processed images or videos, the detected human body information is used for identifying key points and gesture information of human bones through the gesture estimation model, the change of the key points of the bones is observed according to different exercise actions, the frequency in unit time of the exercise actions of the user is clustered, the optimal frequency set in unit time of the exercise of the user is determined, and if the optimal frequency set is counted once, the accuracy of the exercise of the user can be effectively improved, and the exercise effect is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the exercise machine coordinated control system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides a technical solution:
the fitness equipment linkage control system comprises a data acquisition sensing module, a camera shooting module, a music playing module and a control module, wherein the data acquisition sensing module comprises a face recognition unit, a fingerprint recognition unit and a motion data sensing unit, the face recognition unit and the fingerprint recognition unit establish a user fitness database based on user identity information, and the motion data sensing unit establishes the user fitness database after acquiring optimal motion data based on a clustering algorithm;
The camera module is used for identifying and detecting gesture information of key points of human bones after preprocessing a shot image or video, and counting the moving gestures after determining the linkage change range of the key points of the bones;
The music playing module is used for playing songs interested by the user in the exercise process of the user after the user listening log is imported into the exercise database of the user.
The face recognition unit and the fingerprint recognition unit are used for acquiring identity information of a user and generating corresponding user ID serial numbers, binding and matching of the user information and the acquired user body-building information in the body-building database are realized through the user ID serial numbers, the face recognition unit and the fingerprint recognition unit comprise face recognition equipment and fingerprint acquisition equipment at an entrance of a body-building room and on the body-building equipment, and the face recognition equipment and the fingerprint acquisition equipment are connected with terminal equipment through network signals.
The motion data sensing unit comprises a counting sensor, light equipment and a control module electrically connected with the counting sensor, wherein the control module clusters the frequency of the user body-building action and determines the optimal frequency set when the user body-building action.
The control module comprises a host terminal of the gymnasium, the host terminal is connected with face recognition equipment and fingerprint acquisition equipment in the data acquisition sensing module through network signals, and the host terminal is electrically connected with a counting sensor and light equipment in the motion data sensing unit.
The control module generates corresponding control instructions according to the received real-time frequency values of the body-building equipment by judging whether the frequency of the user during body-building is in the optimal frequency aggregate set, and controls the real-time color state of the light equipment through the control instructions.
The real-time color state of the light equipment is divided into three colors of red, green and blue, when the frequency of the user during exercise is lower than the optimal frequency set, the real-time color of the light equipment is controlled to be blue by the instruction sent by the control module, when the frequency of the user during exercise is higher than the optimal frequency set, the real-time color of the light equipment is controlled to be red by the instruction sent by the control module, and when the frequency of the user during exercise is in the optimal frequency set, the real-time color of the light equipment is controlled to be green by the instruction sent by the control module.
The fitness database includes a data storage processing device that stores fitness data information only 30 days prior to a single user login time.
After the camera module determines the identity of a user based on a face detector, transmitting a shot image and a shot video to a processor for image preprocessing, detecting and positioning an object from the processed image or video, identifying key points and gesture information of human bones according to detected human body information through a gesture estimation model, observing the change of the key points of the human bones according to different body-building actions, wherein the key points of the human bones refer to 16 specific positions of the head, the shoulder, the knee, the ankle and the like of the human body, and determining the change range by observing the angle change of the key points in a certain body-building action; the camera module is electrically connected with the display module of the body-building equipment through the counting sensor by counting the camera module by using a counting algorithm, and the camera module counts when the follow-up body-building action is in a variation range.
A linkage control method of fitness equipment comprises the following steps:
S1: when a user enters a gymnasium, face recognition equipment and fingerprint recognition equipment at the entrance of the gymnasium can recognize the user, acquire identity information of the user and generate corresponding user ID serial numbers, a user gymnasium database is built by using the serial numbers, and binding and matching of user information and acquired user gymnasium information in the gymnasium database are realized through the user ID serial numbers;
S2: the user can import own song listening log into the music playing module of the exercise equipment, can play music according to own preference in the exercise process, can also use the equipment to recommend music playing according to the song listening log of the user,
S3: and after the camera module determines the identity of the user based on the face detector, the camera module transmits the shot image and video to a processor for image preprocessing.
The shot pictures are transmitted to an image processor, the image processor acquires vectors after VLAD fusion of image data, firstly, clusters all frame features of one video to obtain a plurality of cluster centers, all features are distributed to the designated cluster centers, the feature vectors in each cluster region are averaged, and finally, the feature vectors of all the cluster regions are combined to serve as the feature vectors of the whole video. The feature vectors of all frames are clustered to one central point, and the VLAD clusters the feature vectors of all frames to a plurality of central points, so that richer features can be obtained and less information is lost through stacking of all the feature vectors. The obtained vector models the image through a low-rank approximation model in 3D convolution, and a characteristic result is obtained.
A convolution kernel of 3 x 3 is used for all images and the convolution layer step size is set to 1. The input to the VGG is set to an RGB image of 224 x 244 size, the RGB average is calculated over all images on the training set image, and then the images are passed as input into the VGG convolutional network.
Assuming that n recognition points are extracted in one image, the visual feature of this image may be represented as χ= { x 1,...,xj,…xn }, and the corresponding position feature may be represented as p=p= { P 1,…,Pj,…,Pn }, where the x j and P j distributions represent the visual feature and the position feature of the j-th recognition point. In order to obtain a more efficient representation of the recognition, it is necessary to encode the underlying image features to obtain a vector-level representation of the image.
Firstly, clustering visual features by using k-means clustering to generate a visual dictionary, and assuming that the size of the visual dictionary VD 1 obtained by clustering is k 1, then VD 1 is represented as VD 1＝{C1,…,Ci,…,Ck1, wherein C i represents the ith clustering center in the visual dictionary, and the original VLAD coding is to calculate the sum of differences between each clustering center and the elements, namely, the coding vector of the ith clustering center is represented as:
Where x j represents the j-th visual feature contained in cluster center C i, and N i represents the number of visual features contained in cluster center C i. In order to enhance the effect of the cluster center in the visual feature, the calculation of the residual error between the cluster center and the most similar element is added, and the VLAD calculation formula of the ith cluster center is as follows: Where x t represents the visual features most similar to the visual features contained in cluster center C i. In summary, when the size of the visual dictionary is k 1, the image coding vector of the image can be expressed as:
The image data is enhanced, the amplitude is enhanced randomly, the data x valid is obtained after the effective image data extraction is completed, the sampling point number is N valid, N clip is taken as the sampling point number of each image, and the x valid is sliced to obtain a plurality of slice data x clip(λc), wherein lambda c is the slice sequence number.
Random amplitude enhancement or attenuation is performed for each slice data x clip(λc). First, a gain value rn_s is randomly selected from [ rn_l, rn_h ] (for example [ -10,10 ]), then a second random value is selected from [ rn_s-rn_o, rn_h+rn_o ] to be used as the gain of a slice, and the gain value rn_s is directly multiplied with slice data to obtain slice data after gain processing. The gain values of all slices are randomly acquired in the range of [ rn_s-rn_o, rn_h+rn_o ], and gain processing is performed in the same manner. Where rn_l and rn_h are the lower and upper limits, respectively, of the first stage random gain, and rn_o is the offset of the second stage gain. The data slices after the random gain processing are recombined into complete image data.
Detecting and positioning objects from the processed images or videos, identifying key points and posture information of human bones through a posture estimation model according to the detected human body information, observing the change of the key points of the bones according to different body-building actions, and counting the key points by using a counting algorithm;
Extracting global features, extracting Rx and Ry nodes, up-sampling the extracted feature map, wherein an up-sampling module consists of 3 continuous deconvolution layers, batch normalization and ReLU activation functions, generating a 2D heat map of root nodes through 1X 1 convolution, and extracting the Rx and Ry nodes from the 2D heat map by Softargmax. The method is used for extracting RZ nodes, the feature map extracted by the back plane layer is subjected to global average pooling treatment, and then a final absolute depth value RZ is obtained by multiplying a convolution output scalar value gamma of 1 multiplied by a k value.
Different body-building actions have different body-building counting algorithms, and the difference is that the key points of the human skeleton change in a linkage way, and the angle change of a certain key point is observed. Assuming that coordinates of three bone key points are (1, yxA), (2, yxB) and (3, yxC) respectively, calculating side lengths corresponding to the angles are a, B and c respectively, and judging whether the angle B is in a selectable angle range or not through an inverse cosine formula.
Taking high-level pull-down as an example, a rule of counting high-level pull-down body-building actions is defined, and the requirement of the high-level pull-down body-building actions is strictly complied, namely, the upper trunk is ensured to be in a straight line state, and the counting is carried out only when the arm states are changed within a specific angle range of two elbows in the pull-down process.
S4: in the user body building process, a counting sensor on the body building equipment calculates the action frequency of the user body building, a control module clusters the frequency in the unit time of the user body building action, an optimal frequency set in the unit time of the user body building is determined, if the optimal frequency set is in the optimal frequency set, the counting is carried out once and is displayed on a display screen of the body building equipment, and otherwise, the counting is not displayed.
The specific steps of counting are as follows:
The following definitions are given first:
The average value of the distance between any two frequency data points is:
Wherein: d (x i,xj) is the Euclidean distance between any two points, n represents the number of frequency data points.
The radius of the field is defined as R,Wherein reler is an adjustment coefficient, and the clustering effect is best when reler is 0.13.
The aggregation degree of frequency points defines:
cluster mean distance is defined as:
G (x i) is determined by comparing the degree of aggregation Dp (x i), which is used to measure the variability between different clusters. Of all frequency points, G (x i) is the maximum distance between x i and all remaining points when the aggregation of x i is maximum, and vice versa is the minimum distance between x i and all remaining points.
The center aggregation parameter is defined as:
Calculating a center aggregation parameter omega (X i) of each point;
The removal of free points, which are points that are far from all other points, whose presence can lead to deviations from the center point in the class, thus affecting the classification.
Selecting a point X i with the maximum omega (X i) as a first initial clustering center, calculating the distance between X i and the rest points, comparing the obtained distance value with a neighborhood radius R, if the distance is smaller than R, the points can be classified as X i, so that the points are removed from the data points, if the distance is larger than R, the points are excessively far away from X i and are not suitable as X i, and therefore, the points are reserved for the next step;
Selecting the point with the maximum omega (X i) from the reserved points as a2 nd clustering center, and operating the steps again;
The above steps are repeated until all the points X 1,X2,…Xn in the data set are removed, and k optimal initial centers M i (i=1, 2, …, k) are output.
After counting, the display screen of the exercise equipment is displayed, and only after the counting reaches the exercise requirement, the display screen can be displayed, so that the accuracy of exercise actions of a user can be controlled.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The utility model provides a health and fitness facilities coordinated control system, includes data acquisition sensing module, camera module, music play module and control module, its characterized in that: the data acquisition sensing module comprises a face recognition unit, a fingerprint identification unit and a motion data sensing unit, wherein the face recognition unit and the fingerprint identification unit establish a user body-building database based on user identity information, and the motion data sensing unit establishes the user body-building database after acquiring optimal motion data based on a clustering algorithm;
The camera module is used for identifying and detecting gesture information of key points of human bones after preprocessing a shot image or video, and counting the moving gestures after determining the linkage change range of the key points of the bones;
the music playing module plays songs interested by the user in the user body building process after the user listening log is imported into the user body building database.
2. The exercise machine coordinated control system of claim 1, wherein: the face recognition unit and the fingerprint recognition unit are used for acquiring identity information of a user and generating corresponding user ID serial numbers, and binding and matching of the user information and the acquired user body-building information in the body-building database are achieved through the user ID serial numbers.
3. The exercise machine coordinated control system of claim 1, wherein: the face recognition unit and the fingerprint recognition unit comprise face recognition equipment and fingerprint acquisition equipment at the entrance of the gymnasium and on the gymnasium, and the face recognition equipment and the fingerprint acquisition equipment are connected with the terminal equipment through network signals.
4. The exercise machine coordinated control system of claim 1, wherein: the motion data sensing unit comprises a counting sensor and light equipment, wherein the counting sensor clusters the frequency of the user body-building action after transmitting data to the terminal equipment, and determines the optimal frequency aggregate during the user body-building.
5. The exercise machine coordinated control system of claim 4, wherein: the control module generates corresponding control instructions according to the received real-time frequency values of the body-building equipment by judging whether the frequency of the user during body-building is in the optimal frequency aggregate, and controls the real-time color state of the light equipment through the control instructions.
6. The exercise machine coordinated control system of claim 5, wherein: the real-time color state of the light equipment is divided into three colors of red, green and blue, when the frequency of the user during exercise is lower than the optimal frequency set, the real-time color of the light equipment is controlled to be blue by the instruction sent by the control module, when the frequency of the user during exercise is higher than the optimal frequency set, the real-time color of the light equipment is controlled to be red by the instruction sent by the control module, and when the frequency of the user during exercise is in the optimal frequency set, the real-time color of the light equipment is controlled to be green by the instruction sent by the control module.
7. The exercise machine coordinated control system of claim 1, wherein: after the camera module determines the identity of the user based on the face detector, the camera module transmits the shot image and video to the processor for image preprocessing, detects and positions objects from the processed image or video, recognizes key points and gesture information of human bones through the gesture estimation model according to the detected human body information, observes the change of the key points of the bones according to different body-building actions, and counts the changes by using a counting algorithm.
8. The exercise machine coordinated control system of claim 7, wherein: the key points of the human bones refer to a plurality of specific positions of the head, the shoulders, the knees and the ankles of the human body, and the change range is determined by observing the angle change of the key points in a certain body-building action.
9. The exercise machine coordinated control system of claim 8, wherein: the control module comprises a host terminal of the gymnasium, the host terminal is connected with face recognition equipment and fingerprint acquisition equipment in the data acquisition sensing module through network signals, and the host terminal is electrically connected with a counting sensor and lamplight equipment in the motion data sensing unit.
10. The exercise machine coordinated control method according to any one of claims 1 to 9, comprising the steps of:
S1: when a user enters a gymnasium, face recognition equipment and fingerprint recognition equipment at the entrance of the gymnasium can recognize the user, acquire identity information of the user and generate corresponding user ID serial numbers, a user gymnasium database is built by using the serial numbers, and binding and matching of user information and acquired user gymnasium information in the gymnasium database are realized through the user ID serial numbers;
S2: the user can import own song listening logs into the music playing module of the exercise equipment, and can play music according to own preference in the exercise process;
S3: after the camera module determines the identity of the user based on the face detector, the camera module transmits the shot image and video to the processor for image preprocessing, detects and positions objects from the processed image or video, recognizes key points and gesture information of human bones through the gesture estimation model according to the detected human body information, observes the change of the key points of the bones according to different body-building actions, and counts the changes by using a counting algorithm;
s4: in the user body building process, a counting sensor on the body building equipment calculates the action frequency of the user body building, a control module clusters the frequency in the unit time of the user body building action, an optimal frequency set in the unit time of the user body building is determined, if the optimal frequency set is in the optimal frequency set, the counting is carried out once and is displayed on a display screen of the body building equipment, and otherwise, the counting is not displayed.
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