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

Sure Steps: Key Strategies for Protecting Basketball Players from Injuries-A Systematic Review

Background: Basketball is a high-intensity sport, which includes actions such as jumping, changes of direction, accelerations, and decelerations, which generates fatigue situations that may increase the risk of injury. Specifically, the joints at greatest risk are the ankle and knee, with ankle sprains and anterior cruciate ligament (ACL) tears being the most prevalent injuries. There are several strategies aimed at reducing the incidence, based on training methods or other prophylactic measures. Therefore, the purpose of the study is to perform a systematic review of the different injury prevention strategies in competitive-level basketball players with respect to general injuries, ankle sprains, and ACL injuries. Methods: For this purpose, the PRISMA methodology was applied, performing a search in three databases (PubMed, SPORTDiscus, and Cochrane) between 25 September 2023 and 8 October 2023. Results: A total of 964 articles were identified, out of which 283 were duplicates and 644 were discarded. Out of the remaining 37, 23 were excluded because they did not meet the inclusion criteria; therefore, 14 articles were finally included. With respect to general injuries, 8 out of 14 studies reviewed them. Concerning ankle sprains, 7 studies specifically analyzed them. Finally, 3 studies focused on ACL injuries. Conclusions: Training programs that combine different contents, known as neuromuscular training, including strength work, stabilization or core, mobility, and agility are the most effective for both general injuries and ACL injuries. For ankle sprains, the most effective measures are training programs based on analytical ankle stability exercises and the use of ankle braces. Adherence to prevention programs is essential, so they can be included as part of the warm-up. Other strategies such as training load control, functional assessment, or rule modification are not used in the included articles, so their effectiveness as prophylactic methods could not be justified.

The Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

INTRODUCTION: Basketball involves a wide variety of complex human motions. Thus, recognizing them with Precision is essential for both training and competition. The subjective perceptions and experiences of the trainers are heavily relied upon while training players. Big data and Artificial Intelligence (AI) technology may be utilized to track athlete training. Sensing their motions may also help instructors make choices that dramatically improve athletic ability. OBJECTIVES: This research paper developed an Action Recognition technique for teaching basketball players using Big Data, and CapsNet called ARBIGNet METHODS: The technique uses a network that is trained using large amounts of data from basketball games called a Whale Optimized Artificial Neural Network (WO-ANN) which is collected using capsules. In order to determine the spatiotemporal information aspects of basketball sports training from videos, this study first employs the Convolution Random Forest (ConvRF) unit. The second accomplishment of this study is creating the Attention Random Forest (AttRF) unit, which combines the RF with the attention mechanism. The study used big data analytics for fast data transmissions. The unit scans each site randomly, focusing more on the region where the activity occurs. The network architecture is then created by enhancing the standard encoder-decoder paradigm. Then, using the Enhanced Darknet network model, the spatiotemporal data in the video is encoded. The AttRF structure is replaced by the standard RF at the decoding step. The ARBIGNet architecture is created by combining these components. RESULTS: The efficiency of the suggested strategy implemented on action recognition in basketball sports training has been tested via experiments, which have yielded 95.5% mAP and 98.8% accuracy.

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.

MPPTM: A Bio-Inspired Approach for Online Path Planning and High-Accuracy Tracking of UAVs

The path planning and tracking problem of the multi-robot system (MRS) has always been a research hotspot and applied in various fields. In this article, a novel multi-robot path planning and tracking model (MPPTM) is proposed, which can carry out online path planning and tracking problem for multiple mobile robots. It considers many issues during this process, such as collision avoidance, and robot failure. The proposed approach consists of three parts: a neural dynamic path planner, a hyperbolic tangent path optimizer, and an error-driven path tracker. Assisted by Ultra-wideband positioning system, the proposed MPPTM is a low-cost solution for online path planning and high-accurate tracking of MRS in practical environments. In the proposed MPPTM, the proposed path planner has good time performance, and the proposed path optimizer improves tracking accuracy. The effectiveness, feasibility, and better performance of the proposed model are demonstrated by real experiments and comparative simulations.

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot’s sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.

Research on sports aided teaching and training decision system oriented to deep convolutional neural network

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.