CN113657237A - Weight lifting motion analysis system based on vision - Google Patents
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- CN113657237A CN113657237A CN202110916792.7A CN202110916792A CN113657237A CN 113657237 A CN113657237 A CN 113657237A CN 202110916792 A CN202110916792 A CN 202110916792A CN 113657237 A CN113657237 A CN 113657237A
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- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B21/00—Exercising apparatus for developing or strengthening the muscles or joints of the body by working against a counterforce, with or without measuring devices
- A63B21/06—User-manipulated weights
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- A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
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Abstract
The invention provides a weight lifting motion analysis system based on vision, which comprises the following components: the system comprises data acquisition equipment, an operation interface, an analysis module and a data storage module; the data acquisition equipment acquires the front and side video data of the athlete and transmits the data to an operation interface; the operation interface displays data and transmits the data to the analysis module through a transmission protocol; the analysis module preprocesses data through a space attention mechanism SAM, realizes athlete key point tracking and various motion data analysis by utilizing a deep neural network, and stores the data to the data storage module; and the analysis module forms an analysis report according to the key points and various motion data and transmits the analysis report to the operation interface. The invention greatly reduces the operation amount, fills the gap in similar application of motion analysis, and supports concurrent processing of simultaneous operation of multiple users by adopting containerization deployment.
Description
Technical Field
The invention relates to the field of motion analysis, in particular to a weight lifting motion analysis system based on vision.
Background
The sports data analysis industry is mainly focused on information statistics such as scores and tactics of multi-person projects at present, a small number of single-person motion analysis systems using computer vision are focused on a motion video editing function, and accurate data are not acquired and are not combined with kinematic analysis.
The existing motion analysis system achieves action decomposition through video unframing in a single project, and achieves track reappearance through timing sequence key point marking and smooth curve drawing.
The disadvantages of the method are as follows: decomposing the motion according to frames increases the work load of the practical coach, and the coach needs to pay the same attention to each frame to observe the motion change; the real time sequence labeling of the key points to draw the track is that the user draws on the video instead of the real tracking and displaying of the key points, and the difference between the drawn curve and the real track is large.
Meanwhile, the conventional single-person motion analysis system often uses a traditional video analysis method, has the problems of being not accurate enough, being incapable of automatically positioning an analysis object and the like, only feeds back measurement data, does not perform data fusion analysis to generate a report, and is not intuitive and difficult to analyze for a user.
Disclosure of Invention
The invention belongs to an image detection technology based on a computer vision technology. The computer vision is used for simulating the function of human vision, and the image is acquired, processed, calculated, and finally actually detected, controlled and applied from a specific real object.
The invention provides a weight lifting motion analysis system based on vision, which comprises the following steps:
the system comprises data acquisition equipment, an operation interface, an analysis module and a data storage module;
the data acquisition equipment acquires the front and side video data of the athlete and transmits the data to an operation interface; the operation interface displays data and transmits the data to the analysis module through a transmission protocol;
the analysis module preprocesses data through a space attention mechanism SAM and realizes player key point tracking and various motion data analysis by utilizing a deep neural network;
and the analysis module forms an analysis report according to the key points and various motion data and transmits the analysis report to the operation interface.
Further, the transmission protocol comprises MQTT and FTP protocols.
Further, the pretreatment specifically comprises: the spatial attention mechanism SAM is used to select the subject of each frame of the data.
Further, the method utilizes a deep neural network to realize the tracking of the key points of the athlete, and specifically comprises the following steps: extracting features of each frame of picture main body by adopting VggNet, identifying 18 key points of a human body by combining context semantics, and determining the relative position of each frame of picture where the key points are located; and storing two-dimensional information data by adopting a key point thermodynamic diagram PCM representation.
Further, the items of motion data include: knee angle, arm angle, barbell acceleration, and center acceleration.
Further, each item of motion data is obtained through key points and preset acquisition equipment position information.
Further, the motion data analysis includes: the athlete can concentrate the strength of the whole body when getting out of arm, begin and end the action of the second part of lifting, tighten the body at the end of the lifting movement and begin and end the action of putting down the barbell.
The forming process of the analysis report specifically comprises the following steps: a model is built through time sequence change of various motion data, the model is combined with historical data to serve as input of a motion state analysis neural network, and multi-dimensional data fusion is achieved by utilizing the signal processing capacity of the artificial neural network.
The beneficial effects provided by the invention are as follows:
1. the main body selection preprocessing is carried out on the video through a space attention mechanism, so that the operation amount is reduced to the great extent;
2. the real track of key points in the motion process of the athlete is generated by the posture estimation technology, and the gap in similar application is filled;
3. combining the reverse dynamics and the weightlifting motion theory, and increasing the motion decomposition process to a unit of motion process instead of a unit of video frame;
4. carrying out multi-dimensional key information fusion of weightlifting motion through a neural network, and carrying out kinematic analysis by using data acquired from a video and historical data;
5. the system adopts containerization deployment and supports concurrent processing of simultaneous operation of multiple users.
Drawings
FIG. 1 is a block diagram of a vision-based weight lifting motion analysis system of the present invention;
FIG. 2 is a graph showing the trend of knee angle change with time at various stages of weight lifting according to the present invention;
fig. 3 is a graph showing the change of hip angle with time in each stage of weight lifting.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, a system for analyzing weight lifting movement based on vision includes the following:
the system comprises data acquisition equipment, an operation interface, an analysis module and a data storage module;
the data acquisition equipment acquires the front and side video data of the athlete and transmits the data to an operation interface;
the operation interface displays data and transmits the data to the analysis module through a transmission protocol;
the analysis module preprocesses data through a space attention mechanism SAM and realizes player key point tracking and various motion data analysis by utilizing a deep neural network;
and the analysis module forms an analysis report according to the key points and various motion data and transmits the analysis report to the operation interface.
As an embodiment, the system is based on a body segmentation technology of an attitude estimation technology, inverse dynamics, a weightlifting motion analysis theory, an artificial neural network and a space attention mechanism.
The data acquisition equipment collects videos of the front and the side of the weight lifting athlete, and a user uploads the videos to the analysis module through an MQTT and FTP protocol by using an operation interface. The system supports the data interaction of external network users, namely the data interaction does not need to be carried out under the same local area network.
The analysis module obtains the video and then preprocesses the video, and selects a subject to be analyzed in each frame of picture-a region range of the action of the weightlifting athlete through a Spatial Attention Mechanism (SAM).
Extracting the characteristics of each frame of main body Part through a deep neural network VggNet, identifying 18 key points of the human body by combining context semantics, determining the relative positions of the key points of each Part of the human body in each frame, representing and storing two-dimensional information data by using a PCM (Part consistency map, key point thermodynamic diagram), and realizing the tracking of the key points of athletes; a line drawing function is embedded in an analysis algorithm, the track is drawn on a user-defined mask by taking a frame as a unit, and the user-defined tracking display of the track of the key points of the athlete is supported on an operation interface.
According to the key point data and the position information of the collection equipment preset by the system, the motion data of the athlete such as the knee included angle, the arm included angle, the barbell acceleration, the center acceleration and the like in the whole weight lifting process are calculated by combining reverse dynamics, various data information are calibrated by the preset focal length of the collection equipment and the relative position of the equipment and the athlete, and the data information is stored. In the embodiment of the invention, the coordinate descent method is adopted to acquire various motion data.
Based on the kinematic characteristics of weight lifting, the data of the included angle of the arm and the included angle of the knee are combined and analyzed to judge the beginning and the end of the first part of the lifting action, and the concentration degree of the whole body strength of the athlete in lifting is judged by comparing the independent change and the relative change of the time sequence of the two and the time coincidence degree of the maximum angle; through the data combination analysis of the zero acceleration and the gravity center acceleration of the bar, the strength and the tension action time of the exerciser in the process of lifting the barbell are deduced, and the beginning and the end of the action of the second part of the weight lifting are judged; and analyzing the height change of the barbell independently to judge the start and end of actions of tightening the body at the end of the lifting movement and putting down the barbell.
Specifically, the first part of the jack-up starts from the beginning to the end of the athlete squat to the lowest point (point 3), for a total of 6 points:
the time difference between the point 1 (the point where the gravity center coincides with the height of the barbell) and the point 2 (the highest point of the barbell in the first stage) is in negative correlation with the continuous pulling force in the process (the lifting speed of the barbell in the time difference needs to be counted, so that the force is reversely exerted); the included angles of the big arm and the small arm are in negative correlation with the concentration degree of the whole body force at the point 3; point 4 is the barbell and body center of gravity stabilization point in the knee-leading and bell-lifting stage, and the first stage is finished (from preparation to knee-leading and bell-lifting); point 5 is the key point of the second part of action, the starting point of the force stage; point 6 is the end of the force phase, representing the beginning of the inertia up phase; and the time difference between the two points is in negative correlation with the continuous thrust in the process;
referring to FIGS. 2-3, FIG. 2 is a graph showing the trend of knee angle change with time at various stages of weight lifting according to the present invention; fig. 3 is a graph showing the change of hip angle with time in each stage of weight lifting.
And constructing a model by using the calculation results of the data of each part (the height of the barbell, the acceleration of the knee, the included angle of the arm and the central acceleration) in time sequence change, combining the model with historical data to be used as the input of a motion state analysis neural network, and realizing multi-dimensional data fusion by using the signal processing capability and the automatic reasoning function of an artificial neural network to generate a feedback report for a user.
The present invention specifically explains the process as follows:
the invention uses the correlation algorithm to obtain various indexes which have influence on the performance of the athletes, and the indexes are called as 'beneficial indexes'.
In addition, the data acquisition system of the invention can obtain beneficial indexes of each training, the indexes can be used as a multi-dimensional vector, and each training video can obtain the vector.
The invention uses a cluster analysis algorithm and a neural network to predict the value of the beneficial index when the athlete reaches the best performance, and the value can be used as a training target to assist the athlete to improve.
The principle is that the value of the beneficial index of each training of the athlete is not completely unchanged, and the fluctuation of some values can cause the change of the performance. We believe that similar floating trends may yield similar results (better or worse). Therefore, beneficial indexes obtained by multiple times of training can be classified, a class which can make the performance become good is taken out, neural network fitting is carried out, a multidimensional vector is extracted from the class of data, and the vector can be considered as a due index when the athlete can achieve the best performance.
The invention acquires data such as the gravity center height, the barbell speed, the included angle between the knee joint and the hip joint and the like of historical training videos of athletes to obtain a plurality of multidimensional vectors, and each index of each vector represents each acquired data in a training process. We can then use the K-means cluster analysis algorithm to calculate the distance between vectors using Euclidean distances, and classify all data into K classes. The value of K may be selected in conjunction with the classification. The K types have similar beneficial indexes and represent a weight lifting mode.
The invention counts the average weight-lifting achievement of each type, and takes out the type with the best average achievement for the following continuous analysis. Finally, the mode represented by the index is considered to be easier to achieve.
As the training of athletes possibly has a certain habitual defect, the invention introduces the first three match data in each big-sports event final competition, each match data is regarded as a multi-dimensional vector and becomes the champion data, the champion data and the class with the optimal average performance obtained in the previous step are subjected to K-mean cluster analysis again, the average weight lifting performance of each class is counted again, and the class with the optimal average performance is taken out and is called as the optimal class.
And then, discrete data serialization is carried out in the optimal class by utilizing a spline interpolation method. Because the density of points in a class is high, the error of the continuously obtained data is small. And finally, well fitting the continuous key data distribution condition in the optimal class and the corresponding prediction achievement thereof. The continuous class becomes a continuous optimal class.
In the invention, the artificial Neural Network performs optimization calculation in a continuous optimal class by utilizing a LeNet-AlexNet-VGG Network in an RNN (Current Neural Network). The weighted gradient is decreased by utilizing a RMSProp (root mean square transfer) algorithm, and the point with the highest performance in the optimal class is obtained in the process, so that the optimal performance which can be achieved by the athlete can be predicted to reach each key data index of the performance.
After the processing is finished, the analysis module informs a user that the system is finished through an MQTT protocol, and sends a data processing result and the line drawing mask information to an operation interface, so that the user can display the analysis result through the operation interface and support to custom check each item of data and each key point track.
In the invention:
1. data acquisition equipment for acquiring video information
2. The operation interface is used for uploading and playing back videos and presenting analysis reports
3. The analysis module is used for acquiring data information of the athlete from the video, analyzing the training process of the athlete by combining a kinematics theory and generating a report;
4. the data storage module is used for storing historical data and historical analysis results of the athletes.
The invention has the beneficial effects that:
1. the main body selection preprocessing is carried out on the video through a space attention mechanism, so that the operation amount is reduced to the great extent;
2. the real track of key points in the motion process of the athlete is generated by the posture estimation technology, and the gap in similar application is filled;
3. combining the reverse dynamics and the weightlifting motion theory, and increasing the motion decomposition process to a unit of motion process instead of a unit of video frame;
4. carrying out multi-dimensional key information fusion of weightlifting motion through a neural network, and carrying out kinematic analysis by using data acquired from a video and historical data;
5. the system adopts containerization deployment and supports concurrent processing of simultaneous operation of multiple users.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A vision-based weight lifting motion analysis system, characterized by: the method comprises the following steps:
the system comprises data acquisition equipment, an operation interface, an analysis module and a data storage module;
the data acquisition equipment acquires the front and side video data of the athlete and transmits the data to an operation interface;
the operation interface displays data and transmits the data to the analysis module through a transmission protocol;
the analysis module preprocesses data through a space attention mechanism SAM, realizes athlete key point tracking and various motion data analysis by utilizing a deep neural network, and stores the data to the data storage module;
and the analysis module forms an analysis report according to the key points and various motion data and transmits the analysis report to the operation interface.
2. A vision-based weight lifting motion analysis system as claimed in claim 1 wherein: the transmission protocol comprises MQTT and FTP protocols.
3. A vision-based weight lifting motion analysis system as claimed in claim 1 wherein: the pretreatment specifically comprises the following steps: the spatial attention mechanism SAM is used to select the subject of each frame of the data.
4. A vision-based weight lifting motion analysis system as claimed in claim 3 wherein: the method for realizing the tracking of the key points of the athletes by utilizing the deep neural network specifically comprises the following steps: extracting features of each frame of picture main body by adopting VggNet, identifying 18 key points of a human body by combining context semantics, and determining the relative position of each frame of picture where the key points are located; and storing two-dimensional information data by adopting a key point thermodynamic diagram PCM representation.
5. A vision-based weight lifting motion analysis system as claimed in claim 1 wherein: the items of motion data include: knee angle, arm angle, barbell acceleration, and center acceleration.
6. A vision-based weight lifting motion analysis system as claimed in claim 5 wherein: each item of motion data is obtained through key points and preset acquisition equipment position information.
7. A vision-based weight lifting motion analysis system as claimed in claim 1 wherein: the motion data analysis includes: the athlete can concentrate the strength of the whole body when getting out of arm, begin and end the action of the second part of lifting, tighten the body at the end of the lifting movement and begin and end the action of putting down the barbell.
8. A vision-based weight lifting motion analysis system as claimed in claim 1 wherein: the forming process of the analysis report specifically comprises the following steps: a model is built through time sequence change of various motion data, the model is combined with historical data to serve as input of a motion state analysis neural network, and multi-dimensional data fusion is achieved by utilizing the signal processing capacity of the artificial neural network.
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