CN113617004A - Training method for improving shooting hit rate of basketball players - Google Patents
Training method for improving shooting hit rate of basketball players 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
- A63B69/00—Training appliances or apparatus for special sports
- A63B69/0071—Training appliances or apparatus for special sports for basketball
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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
- A63B2220/00—Measuring of physical parameters relating to sporting activity
- A63B2220/20—Distances or displacements
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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
- A63B2220/00—Measuring of physical parameters relating to sporting activity
- A63B2220/40—Acceleration
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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
- A63B2230/00—Measuring physiological parameters of the user
- A63B2230/60—Measuring physiological parameters of the user muscle strain, i.e. measured on 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
- A63B2230/00—Measuring physiological parameters of the user
- A63B2230/62—Measuring physiological parameters of the user posture
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Abstract
The invention relates to a training method for improving the shooting hit rate of a basketball player, belonging to the technical field of basketball sports, wherein data of upper limbs and hands of the basketball player during shooting action are collected and summarized into an upper limb shooting action information database, indexes in the upper limb shooting action information database are subjected to statistical analysis according to an analytic hierarchy process, shooting number, hit rate, fatigue, muscle activity, curvature, displacement and acceleration indexes of fingers are comprehensively analyzed, relative weights among the indexes are obtained, a final training evaluation score is obtained according to the data weighting of the indexes, and the basketball player adjusts own shooting action according to the evaluation score and the index data to improve the shooting hit rate. Aiming at the shooting training of the penalty line, the invention has obvious effect on the fixed-point shooting training; aiming at the physical quality difference of the athletes, various index data are acquired, so that the athletes can quickly achieve the training purpose, the training mode of the athletes is changed, and the shooting hit rate of the athletes is improved.
Description
Technical Field
The invention relates to a training method for improving the shooting hit rate of basketball players, belonging to the technical field of basketball sports.
Background
Basketball is a very popular competitive game, where players win the game with a high score for the purpose of scoring the shot. In recent years, the Chinese basketball game is advancing forward in a big step, and the ability of improving the scores of basketball players is more and more important when various levels of basketball match are compared in a scale and a bar. Therefore, how to effectively train basketball players and improve shooting hit rate is very important, especially for making personalized shooting training plans.
The final action of the player when shooting is the key to accurately hit the shot, and the direction, strength, radian and rotation of the shot are directly influenced. The hands-off act includes a proper shooting method and coordinated exertion throughout the body, with the shooting method again being the most critical. The shooting method is mainly characterized in that the shooting method is in-situ shooting and comprises shooting in front of two hands and chests, shooting on two hands and shoulders and shooting on one hand and shoulders, and the shooting method mainly comprises the differences of shooting angle of a shooting hand, speed of the shooting hand, strength of the shooting hand, application skills of fingers and wrists and the like. Taking the left hand to shoot on the shoulder in situ with one hand as an example, a player holds the ball with two hands, leads the ball to the front upper part of the right shoulder, bends the elbow of the right arm, keeps the upper arm horizontal with the shoulder joint, makes the forearm and the upper arm at 90 degrees, opens the five fingers of the right hand, bends the wrist backwards, supports the back lower part of the ball with the parts above the palm and the finger root, holds the ball with the left hand, and shoots a basketball. In actual training, players and coaches can only determine the indexes of the shooting action, such as the hand-in angle, the strength and the like according to subjective feeling and experience, so that the training effect is poor, and the training is worried that various data of the shooting action cannot be obtained.
Disclosure of Invention
The invention aims to provide a training method for improving the shooting hit rate of basketball players, which can improve the shooting hit rate of the basketball players, and also can enable a coach to master the training conditions of the basketball players according to index data and reasonably arrange training tasks.
In order to achieve the purpose, the invention adopts the technical scheme that:
the training method for improving the shooting hit rate of basketball players comprises the steps of collecting data of upper limbs and hands of the basketball players during shooting actions and summarizing the data into an upper limb shooting action information database, carrying out statistical analysis on indexes in the upper limb shooting action information database according to an analytic hierarchy process, comprehensively analyzing shooting numbers, hit rates, fatigue degrees, muscle activity degrees, bending degrees of fingers, displacement and acceleration indexes to obtain relative weights among the indexes, weighting according to the data of the indexes to obtain final training evaluation scores, and adjusting the shooting actions of the basketball players according to the evaluation scores and the index data to improve the shooting hit rate.
The technical scheme of the invention is further improved as follows: the device for collecting the hand data of the upper limbs of the athletes during shooting comprises a HIPLAY bracelet, a myoelectric collecting device and a Neuron inertial data collecting glove, wherein the myoelectric collecting device comprises a Delsys wireless collecting module and a base station; the method comprises the following specific steps:
s1: the method comprises the following steps that a player wears a HIPPLAY bracelet, a Delsys wireless acquisition module and Neuron inertial data acquisition gloves, the Delsys wireless acquisition module is connected with an upper computer through a base station, the HIPPLAY bracelet and the Neuron inertial data acquisition gloves are connected with the upper computer through wireless signals, and after the HIPPLAY bracelet and the Neuron inertial data acquisition gloves are prepared, penalty line shooting training is carried out;
s2: setting up a shooting requirement on a penalty line, requiring players to shoot for multiple times on the penalty line, and sending signals acquired by a Delsys wireless acquisition module to an upper computer for preprocessing by a base station to extract muscle activity characteristics;
s3: the upper computer predicts muscle strength of the preprocessed signals through an established multi-scale Convolutional Neural Network (MCNN) model, analyzes the fatigue of the player by combining the shooting times of the HIPLAY bracelet, performs typical Correlation Analysis (CCA) on the shooting quantity, shooting hit rate, muscle strength and muscle activity of the player to obtain the Correlation of the four indexes to the fatigue of the player, and acquires the finger bending, displacement and acceleration information of the player during shooting action through the Neuron inertial data acquisition glove;
s4: the upper computer collects all the collected data information into an upper limb shooting action information database, statistical analysis is carried out on the shooting number, the shooting hit rate, the fatigue degree, the muscle activity degree, the bending degree of fingers, the displacement and the acceleration indexes according to an analytic hierarchy process to obtain the relative weight among the indexes, final training evaluation scores are obtained according to the data weighting of the indexes, and the athletes adjust own shooting actions according to the evaluation scores and the index data to improve the shooting hit rate.
The technical scheme of the invention is further improved as follows: the HIPPLAY bracelet can obtain the number of the basketball shooting of the athlete, and the hit rate of the athlete is obtained by combining with a matched scoring device.
The technical scheme of the invention is further improved as follows: the Delsys wireless acquisition module is attached to Biceps Brachii (BB), Flexor Carpi Radialis (FCR), Extensor Carpi Ulnaris (ECU), Triceps Brachii (TB), Extensor Carpi Radialis (ECR), brachioradialis (B) and flexor carpi ulnaris (ECU) of a subject, acquires myoelectric signals of upper limb muscles and then is connected with an upper computer through a base station to transmit the signals.
The technical scheme of the invention is further improved as follows: the preprocessing of step S2 is to filter, amplify and remove baseline wander processing on the acquired signal.
The technical scheme of the invention is further improved as follows: the training evaluation score formula obtained in step S4 is:
training evaluation score ═ a × number of shots shot + b × hit rate + c × degree of fatigue + d × degree of muscle activity + e × degree of flexion of finger + f × displacement of finger + g × acceleration of finger
Wherein, a, b, c, d, e, f, g are the weighting coefficients of each corresponding technical index.
Due to the adoption of the technical scheme, the invention has the following technical effects:
the invention aims at the shooting training of the penalty line and has obvious effect on the fixed-point shooting training.
The invention aims at the physical quality difference of the athletes, acquires various index data, helps the athletes quickly achieve the training purpose, changes the training mode of the athletes and improves the shooting hit rate of the athletes.
According to the invention, the data are acquired by wearing the bracelet and the Neuron inertial data acquisition glove, so that the data information of the upper limb body index of the sportsman can be reflected more accurately.
The invention is also beneficial to the coach, can help the coach to accurately master the physical information of the athlete, reasonably arrange the training time and the training subjects and avoid the fatigue of the athlete.
The invention can obtain the upper limb body index data information of the athlete, collects the collected data information into an upper limb shooting action information database, analyzes the indexes such as shooting number, hit rate, fatigue, muscle activity, finger curvature, displacement, acceleration and the like by using an analytic hierarchy process in management, obtains the indexes such as shooting number, hit rate, fatigue, muscle activity, finger curvature, displacement, acceleration and the like, obtains the relative weight among the indexes, obtains the final training evaluation score according to the data weighting of the indexes, and adjusts the shooting action of the athlete according to the evaluation score and the index data to improve the shooting hit rate.
The invention adds a statistical analysis link, and the analyzed indexes (the number of shots, the hit rate, the fatigue, the muscle activity, the bending degree of fingers, the displacement and the acceleration) have more pertinence to the shooting training, thereby being convenient for athletes to improve the shooting hit rate; assist coaches to help athletes find a suitable training method.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a multi-scale convolutional neural network of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific embodiments:
a training method for improving the shooting hit rate of basketball players is characterized in that equipment for collecting hand data of upper limbs of the basketball players during shooting actions comprises a HIPPLAY bracelet, myoelectric collecting equipment and a Neuron inertial data collecting glove; the electromyographic acquisition equipment comprises a Delsys wireless acquisition module and a base station, wherein the Delsys wireless acquisition module can acquire signals of 0.5-450 Hz. The method comprises the following specific steps:
s1: the player wears the HIPPLAY bracelet, the Delsys wireless acquisition module and the Neuron inertial data acquisition gloves, the Delsys wireless acquisition module passes through basic station and host computer connection, HIPPLAY bracelet and Neuron inertial data acquisition gloves pass through radio signal and host computer connection, carry out the training of throwing the basket of penalty line after preparing.
S2: the method comprises the steps of establishing a shooting requirement on a penalty line, requiring a player to shoot a basketball for multiple times on the penalty line, using a Delsys wireless acquisition module to acquire myoelectric signals of seven muscles (biceps brachii (BB), Flexor Carpi Radialis (FCR), Extensor Carpi Ulnaris (ECU), Triceps Brachii (TB), Extensor Carpi Radialis (ECR), brachioradialis (B) and flexor carpi ulnaris (ECU)) related to the hand movement of an upper limb when the basketball player shoots the basketball on the penalty line, sending the acquired signals to an upper computer through a base station for preprocessing (filtering, amplifying and removing baseline drift), and extracting muscle activity characteristics. The signal preprocessing formula is as follows:
in the formula, vjRepresenting the characteristics of muscle activity, uj(t) represents the electromyographic signal of the jth muscle after pretreatment, rectification and normalization at the time t, RjIs a non-linear parameter between the myoelectric signal and the muscle activity, typically between-3 and 0.
S3: the upper computer performs muscle force prediction on the preprocessed signals through an established multi-scale Convolutional Neural Network (MCNN) model (as shown in fig. 2, the MCNN model includes 3 groups of Convolutional layers (Conv), 3 groups of pooling layers (Pool), and a group of full-connection layers, and the P-Relu activation function is used to enhance the Neural Network after Convolutional layers).
Basketball players wear HIPLAY bracelets to obtain the shooting number of the players, the hit rate of the players is obtained by combining matched scoring devices, data can be obtained in mobile phone apps, information is gathered to an upper limb shooting action information database through an upper computer, meanwhile fatigue degree evaluation is further performed by combining muscle strength and muscle activity indexes, the Correlation of the four indexes to the fatigue degree of the players is obtained according to the shooting number of the players, shooting hit rate, muscle strength and muscle activity (CCA), and the data is gathered to the upper limb shooting action information database through the upper computer; the gloves are worn to acquire the information of the bending degree, the displacement and the acceleration of the fingers of the player during shooting actions, and the information is collected to an upper limb shooting action information database through an upper computer.
S4: the upper computer conducts statistical analysis on the shooting number, the hit rate, the fatigue degree, the muscle activity degree, the curvature degree, the displacement and the acceleration index of the fingers by using an analytic hierarchy process in management, relative weights among all indexes are obtained, final training evaluation scores are obtained according to the data weighting of the indexes, and the athletes adjust own shooting actions according to the index data to improve the shooting hit rate.
The training evaluation score formula is:
training evaluation score ═ a × number of shots shot + b × hit rate + c × degree of fatigue + d × degree of muscle activity + e × degree of flexion of finger + f × displacement of finger + g × acceleration of finger
Wherein, a, b, c, d, e, f, g are the weighting coefficients of each corresponding technical index.
And (3) realizing an analytic hierarchy process:
the algorithm analyzes from top to bottom, firstly analyzes the tightness degree of each index of the decision layer and the target layer to obtain a one-dimensional weight vector, then analyzes the tightness degree of each index of the scheme layer and each index of the decision layer, and finally obtains the scheme with the maximum tightness degree of the scheme layer and the target layer, namely the weight.
1) Building a hierarchical model
First layer (target layer): a, determining a weight coefficient of a training index;
second layer (decision layer): b1 selection of coach 1, B2: selection of coach 2, selection of coach q … …;
third layer (scheme layer): c1: number of shots, C2: hit rate, C3: fatigue, C4: muscle activity, C5: curvature of finger, C6: displacement of finger, C7: acceleration of the finger;
the second level is the rating (important, less important, general, not important, no influence) made by q trainers on the third level index. The third layer is an index for which a weight is to be determined.
2) Structural judgment matrix
And obtaining the importance of each index in the second layer to other indexes according to the job title levels of the trainers (a third-level trainer, a second-level trainer, a first-level trainer, a high-level trainer and a national-level trainer).
Comparing the importance a of the ith element and the e-th element of the second layer relative to the first layerieAnd judging the matrix:
in the equation, qxq is the relative weight between q trainers.
And (4) according to the grade evaluation made by the q coaches, the importance of each index in the third layer to other indexes is obtained.
Comparing the importance b of the z-th element and the x-th element of the third layer relative to the second layerzxAnd judging the matrix:
where q is the coach's q choice and 7x7 is the relative weight between the 7 metrics.
3) Hierarchical single ordering and consistency check
And (3) hierarchical single ordering: determining the degree of closeness of each index in the layer to a certain index in the upper layer, and determining the eigenvector corresponding to the maximum eigenvalue of the matrix as the final weight vector WBqAnd the matrix is required to satisfy the consistency check.
And (3) checking consistency: and (4) carrying out matrix consistency check by using the consistency index CI, the consistency ratio CR <0.1 and the random consistency index RI.
Obtaining the characteristic vector W of the judgment matrix AA、CI、CR，CR<Pass the consistency check at 0.1.
Obtaining a judgment matrix BqCharacteristic vector W ofBq、CI、CR，CR<Pass the consistency check at 0.1.
Index of consistency
Proportion of consistency
In the formula, lambda is the maximum characteristic root, n is the only nonzero characteristic root, and RI is obtained by table look-up. When the consistency ratio CR is <0.1, the degree of inconsistency is considered to be within the allowable range, and the consistency check is passed.
4) Hierarchical Total ordering and consistency check
According to the feature vector WACharacteristic vector WBqListing the total rank of the hierarchy to obtain CI, CR and CR<Pass the consistency check at 0.1. At this time
The weights of the indexes are obtained.
The training plan of the athlete is established once a week, seven times a cycle, and the best training data of each time is recorded. The athlete can find own shooting training conditions (shooting number, hit rate, fatigue, muscle activity, bending degree, displacement and acceleration of fingers, training evaluation scores) according to the index data, and improve the shooting hit rate.
Claims (6)
1. A training method for improving the shooting hit rate of basketball players is characterized in that: collecting data of upper limbs and hands of a player during shooting actions and summarizing the data into an upper limb shooting action information database, carrying out statistical analysis on indexes in the upper limb shooting action information database according to an analytic hierarchy process, comprehensively analyzing shooting numbers, hit rates, fatigue degrees, muscle activity degrees and finger bending degrees, displacement and acceleration indexes, obtaining relative weights among the indexes, weighting according to the data of the indexes to obtain final training evaluation scores, and adjusting own shooting actions of the player according to the evaluation scores and the index data to improve shooting hit rates.
2. A training method for improving the hit rate of a basketball player in shooting according to claim 1, wherein: the device for collecting the hand data of the upper limbs of the athletes during shooting comprises a HIPLAY bracelet, a myoelectric collecting device and a Neuron inertial data collecting glove, wherein the myoelectric collecting device comprises a Delsys wireless collecting module and a base station; the method comprises the following specific steps:
s1: the method comprises the following steps that a player wears a HIPPLAY bracelet, a Delsys wireless acquisition module and Neuron inertial data acquisition gloves, the Delsys wireless acquisition module is connected with an upper computer through a base station, the HIPPLAY bracelet and the Neuron inertial data acquisition gloves are connected with the upper computer through wireless signals, and after the HIPPLAY bracelet and the Neuron inertial data acquisition gloves are prepared, penalty line shooting training is carried out;
s2: setting up a shooting requirement on a penalty line, requiring players to shoot for multiple times on the penalty line, and sending signals acquired by a Delsys wireless acquisition module to an upper computer for preprocessing by a base station to extract muscle activity characteristics;
s3: the upper computer predicts muscle strength of the preprocessed signals through an established multi-scale Convolutional Neural Network (MCNN) model, analyzes fatigue of the player by combining shooting times of the HIPLAY bracelet, and acquires finger bending, displacement and acceleration information of the player during shooting actions through Neuron inertial data acquisition gloves;
s4: the upper computer collects all the collected data information into an upper limb shooting action information database, statistical analysis is carried out on the shooting number, the shooting hit rate, the fatigue degree, the muscle activity degree, the bending degree of fingers, the displacement and the acceleration indexes according to an analytic hierarchy process to obtain the relative weight among the indexes, final training evaluation scores are obtained according to the data weighting of the indexes, and the athletes adjust own shooting actions according to the evaluation scores and the index data to improve the shooting hit rate.
3. A training method for improving the hit rate of a basketball player in shooting according to claim 2, wherein: the HIPPLAY bracelet can obtain the number of the basketball shooting of the athlete, and the hit rate of the athlete is obtained by combining with a matched scoring device.
4. A training method for improving the hit rate of a basketball player in shooting according to claim 2, wherein: the Delsys wireless acquisition module is attached to Biceps Brachii (BB), Flexor Carpi Radialis (FCR), Extensor Carpi Ulnaris (ECU), Triceps Brachii (TB), Extensor Carpi Radialis (ECR), brachioradialis (B) and flexor carpi ulnaris (ECU) of a subject, acquires myoelectric signals of upper limb muscles and then is connected with an upper computer through a base station to transmit the signals.
5. A training method for improving the hit rate of a basketball player in shooting according to claim 2, wherein: the preprocessing of step S2 is to filter, amplify and remove baseline wander processing on the acquired signal.
6. A training method for improving the hit rate of a basketball player in shooting according to claim 2, wherein: the training evaluation score formula obtained in step S4 is:
training evaluation score ═ a × number of shots shot + b × hit rate + c × degree of fatigue + d × degree of muscle activity + e × degree of flexion of finger + f × displacement of finger + g × acceleration of finger
Wherein, a, b, c, d, e, f, g are the weighting coefficients of each corresponding technical index.
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Application publication date: 20211109 |