Artificial Intelligence Technology in Basketball Training Action Recognition

The primary research purpose lies in studying the intelligent detection of movements in basketball training through artificial intelligence (AI) technology. Primarily, the theory of somatosensory gesture recognition is analyzed, which lays a theoretical foundation for research. Then, the collected signal is denoised and normalized to ensure that the obtained signal data will not be distorted. Finally, the four algorithms, decision tree (DT), naive Bayes (NB), support vector machine (SVM), and artificial neural network (ANN), are used to detect the data of athletes' different limb movements and recall. The accuracy of the data is compared and analyzed. Experiments show that the back propagation (BP) ANN algorithm has the best action recognition effect among the four algorithms. In basketball training athletes' upper limb movement detection, the average accuracy rate is close to 93.3%, and the average recall is also immediate to 93.3%. In basketball training athletes' lower limb movement detection, the average accuracy rate is close to 99.4%, and the average recall is immediate to 99.4%. In the detection of movements of upper and lower limbs: the recognition method can efficiently recognize the basketball actions of catching, passing, dribbling, and shooting, the recognition rate is over 95%, and the average accuracy of the four training actions of catching, passing, dribbling, and shooting is close to 98.95%. The intelligent basketball training system studied will help basketball coaches grasp the skilled movements of athletes better to make more efficient training programs and help athletes improve their skill level.

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

Effect of Number of Players and Maturity on Ball-Drills Training Load in Youth Basketball

This study aimed to assess the basketball ball-drills workload analyzing: (1) the effect of varying the number of players involved on physiological and technical demands; (2) the temporal changes in players’ responses across bouts; and (3) the relationship of players’ workload with their maturation status and training age. Twelve young male basketball players (mean ± SD; age 13.9 ± 0.7 years; height 1.76 ± 0.06 m; body mass 65.7 ± 12.5 kg; HR_max 202 ± 8 beat·min⁻¹) completed three bouts of 4 min interspersed by 2 min of passive recovery of two vs. two and four vs. four ball-drills. The mean percentage of HR_max (%HR_max) and ratings of perceived exertion (RPE) were collected. Technical actions (TAs) (dribbles, passes, shots, interceptions, steals, rebounds, and turnovers) were calculated through notational analysis. Players’ genitalia development (GD) and pubic hair (PH) growth were assessed using Tanner scale. Results showed a higher %HR_max (p = 0.018), RPE (p = 0.042), dribbles (p = 0.007), shots (p = 0.003), and rebounds (p = 0.006) in two vs. two compared to four vs. four condition. Furthermore, a statistical difference was found for %HR_max (p = 0.005) and number of passes (p = 0.020) between bouts. In addition, no correlation between GD, PH, and training age with %HR_max, RPE, and TAs was found. These findings suggest that variations of the number of players involved affect ball-drills workload and that ball-drills training intensity varies across bouts. Finally, ball-drills elicit an adequate training stimulus, regardless of players’ maturation status and training age.

The Reasearch on the Anti-Fatigue Effect of Whey Protein Powder in Basketball Training

In order to observe the effects of whey protein powder on hematological indexes of players majoring in physical education in the basketball training, the authors divided the players randomly into a control group and a nutrition group. Athletes complete the 30 minutes quantitative exercise using cycle ergometer respectively before the trial and after one month trial. Then we exsanguinated immediately after exercise, extracted heparin and measured hemoglobin, red blood cell count, hematocrit and mean corpuscular volume and other hematological indices. The results showed that after taking whey protein powder, the HB, RBC, HCT of nutrition group was significantly higher that the control group. This suggests that in high-intensity training, taking whey protein powder can cause changes of HB, RBC and HCT in human body, meanwhile MCV essentially the same. So whey protein powder can improve exercise capacity, and has anti-fatigue effect.