AI‐Driven Soccer Training Optimization Method via Space‐Air‐Ground Integrated Network

The recognition of sports action is an important research subject, which is conducive to the improvement of athletes’ own level. To improve the accuracy of multi-modal data action recognition, based on the Transformer module, this study introduces a multi-head attention mechanism, fuses multi-modal data, and constructs a multi-stream structured object relationship inference network. Based on PointNet++ network and combining five different data fusion frameworks, a motion recognition model that integrates RGB data and 3D skeleton point cloud is constructed. The results showed that the Top-1 accuracy of multi-stream structured object relationship inference network was 42.5% and 42.7%, respectively, which was better than other algorithms. The accuracy of the multi-modal fusion model was improved by 15.6% and 5.1% compared with the single mode, and by 5.4% and 2.6% compared with the dual mode, which showed its superiority in the action recognition task. This showed that the fusion of multi-modal data can provide more abundant information, so as to improve the accuracy of action recognition. The accuracy of the action recognition model combining RGB data and 3D skeleton point cloud was 84.3%, 87.5%, 90.2%, 90.6% and 91.2% after the combination of different strategies, which effectively compensated for the problem of missing information in 3D skeleton point cloud and significantly improved the accuracy of action recognition. With a small amount of data, the Top-1 accuracy of the multi-stream structured object relationship inference network in this study was superior to other algorithms, showing its advantages in dealing with complex action recognition tasks. In addition, the action recognition model that fuses RGB data and 3D skeleton point cloud also achieved higher accuracy, which is better than other algorithms. This study can meet the needs of motion recognition in different scenarios and has certain reference value.

This paper explores the application of artificial intelligence (AI) technology in youth sports training, focusing on skill prediction, performance forecasting, and injury prevention. Through case studies, it demonstrates AI's role in enhancing biomechanical precision, physiological monitoring, and tactical execution during soccer, basketball, and rugby training. Research findings indicate that AI technology significantly improves training efficiency and athlete performance while reducing injury risks through real-time data analysis and feedback. For instance, in soccer training, a convolutional neural network (CNN)-based system achieved over 90% accuracy in motion recognition, substantially reducing technical errors and injury risks. In basketball training, AI-driven physiological monitoring systems lowered the incidence of non-contact injuries by dynamically adjusting training loads. Furthermore, AI technology optimized match strategies for youth soccer teams through real-time tactical decision support. Despite challenges such as cost, coach training, and data privacy, AI applications in youth sports training hold immense potential to enhance training outcomes and athletes' long-term development.

This study aimed to determine the effects of combined resistance training and plyometrics on physical performance in under-15 soccer players. One team (n=20) followed a 6-week resistance training program combined with plyometrics plus a soccer training program (STG), whereas another team (n=18) followed only the soccer training (CG). Strength training consisted of full squats with low load (45-60% 1RM) and low-volume (2-3 sets and 4-8 repetitions per set) combined with jumps and sprints twice a week. Sprint time in 10 and 20 m (T10, T20, T10-20), CMJ height, estimated one-repetition maximum (1RMest), average velocity attained against all loads common to pre- and post-tests (AV) and velocity developed against different absolute loads (MPV20, 30, 40 and 50) in full squat were selected as testing variables to evaluate the effects of the training program. STG experienced greater gains (P<0.05) in T20, CMJ, 1RMest, AV and MPV20, 30, 40 and 50 than CG. In addition, STG showed likely greater effects in T10 and T10-20 compared to CG. These results indicate that only 6 weeks of resistance training combined with plyometrics in addition to soccer training produce greater gains in physical performance than typical soccer training alone in young soccer players.

The development of activity recognition based on multi-modal data makes it possible to reduce human intervention in the process of monitoring. This paper proposes an efficient and cost-effective multi-modal sensing framework for activity monitoring, it can automatically identify human activities based on multi-modal data, and provide help to patients with moderate disabilities. The multi-modal sensing framework for activity monitoring relies on parallel processing of videos and inertial data. A new supervised adaptive multi-modal fusion method (AMFM) is used to process multi-modal human activity data. Spatio-temporal graph convolution network with adaptive loss function (ALSTGCN) is proposed to extract skeleton sequence features, and long short-term memory fully convolutional network (LSTM-FCN) module with adaptive loss function is adapted to extract inertial data features. An adaptive learning method is proposed at the decision level to learn the contribution of the two modalities to the classification results. The effectiveness of the algorithm is demonstrated on two public multi-modal datasets (UTD-MHAD and C-MHAD) and a new multi-modal dataset H-MHAD collected from our laboratory. The results show that the performance of the AMFM approach on three datasets is better than the performance of the video or the inertial-based single-modality model. The class-balanced cross-entropy loss function further improves the model performance based on the H-MHAD dataset. The accuracy of action recognition is 91.18%, and the recall rate of falling activity is 100%. The results illustrate that using multiple heterogeneous sensors to realize automatic process monitoring is a feasible alternative to the manual response.

Video action recognition technology provides technical support for automated monitoring and early event warning, liberating human resources, preventing anomalies in advance, and handling events more promptly. However, the issue of user privacy leakage is also a growing concern. This paper discusses how to conduct action recognition while protecting privacy, proposing a generative adversarial network architecture that combines a multi-scale feature fusion generator with a spatiotemporal consistency discriminator. The network continuously enhances its capabilities through adversarial training strategies to protect facial privacy in videos. This network extracts features at different levels in the video through a multi-scale feature fusion mechanism so that the video after facial privacy protection still maintains a high degree of realism at the same time, to ensure that the accuracy of action recognition is not compromised, the feature enhancement module is designed to enhance action features and inhibit the privacy features significantly C3D is used as the action recognition model to accurately recognize a variety of actions in the video, such as running, jumping, falling, realizing the action analysis of video content. In this paper, the proposed method is evaluated in terms of privacy protection level and action recognition performance. Experiments are conducted on three datasets, LFW, HMDB51, and Hollywood2, and the results show that this framework effectively protects personal information while maintaining high action recognition accuracy.

Hosseini Kakhak, SA, Kianigul, M, Haghighi, AH, Nooghabi, MJ, and Scott, BR. Performing soccer-specific training with blood flow restriction enhances physical capacities in youth soccer players. J Strength Cond Res 36(7): 1972-1977, 2022-This study investigated the effect of soccer training with blood flow restriction (BFR) on physical performance in youth athletes. Nineteen semiprofessional soccer players were randomly assigned to either normal soccer training (ST; n = 9) or soccer training with BFR (STBFR; n = 10). Both groups performed identical activities during a 6-week preseason training phase, either with or without lower limb BFR. Training included soccer-specific drills, small-sided games, plyometrics, and continuous running. Before and after the intervention, players were assessed for leg extension strength and endurance, countermovement jump performance, 40-yd sprint time, change-of-direction (COD) ability, aerobic endurance, and soccer-specific endurance (while dribbling a ball). Significantly larger improvements were observed in the STBFR compared with the ST group for tests of muscular endurance (74.8 ± 34.1% vs. 4.0 ± 14.6%), COD (8.1 ± 3.7% vs. 2.8 ± 4.7%), and aerobic (54.1 ± 19.6% vs. 24.7 ± 27.2%) and soccer-specific endurance (58.4 ± 19.6% vs. 22.7 ± 10.2%). Main effects for time were observed for maximal strength, jumping, and sprinting performance (p < 0.001) but with no group and time interaction. These findings demonstrate that team sport training with BFR can enhance physical qualities that are related to performance in youth soccer players. This application of BFR may improve the adaptive responses of muscles, without having to dedicate additional training time to muscular qualities.

SUMMARYRemote surveillance of large‐scale equipment such as power plants and building complexes is important to prevent serious attacks and troubles. Automatic human action recognition can reduce the burdens of the surveillance. Multiview video is useful for human action recognition, because it provides robustness to the changes of people's appearance by orientation and occlusion. One problem of conventional multiview action recognition is that it requires both detection and tracking before action recognition. Human pose and motion vary depending on the person's action, and such variances may cause detection and tracking error. To solve this problem, previous work has proposed simultaneous action recognition and location estimation for single‐view videos using Hough voting. In this paper, we extend the Hough voting approach to simultaneous multiview action recognition and location estimation. Our proposed method independently casts votes for the action labels and spatiotemporal reference positions of actions in each view and integrates them using homographical transformations in the multiview extension. We evaluated our method and confirmed that it achieved high accuracy in action recognition and location estimation. The contribution of this paper is that it enables multiview action recognition without prior human detection and tracking.

Recent advancements in digital technologies have profoundly transformed healthcare delivery, sports analytics, and physical activity surveillance. The accelerated proliferation of wearable sensing devices and Internet of Things (IoT) infrastructures has facilitated unprecedented large-scale acquisition of multimodal physiological and kinematic data. However, accurate recognition of complex human activities remains a formidable challenge, primarily due to inadequate modeling of the spatiotemporal dependencies inherent in wearable-sensor signals. To address these fundamental limitations, this paper proposed a novel IoT-oriented activity recognition framework predicated on a Convolutional Recurrent Neural Network (CRNN) architecture, engineered to simultaneously model the spatial and temporal characteristics of multimodal wearable-sensor data streams. The proposed framework synergistically integrates Convolutional Neural Networks (CNNs) for hierarchical spatial feature extraction with Recurrent Neural Networks (RNNs) for temporal sequence modeling, thereby enabling more discriminative and robust activity classification. The methodological pipeline comprises several sequential stages: First, multidimensional wearable-sensor datasets are employed, encompassing physiological and inertial measurements including heart rate variability, triaxial accelerometer readings, gyroscopic angular velocity, magnetometer orientation data, and cutaneous temperature signals acquired from multiple anatomical locations. Second, raw sensor signals undergo preprocessing and temporal segmentation procedures to enhance data quality and optimize temporal representation. Third, spatiotemporal feature representations are learned autonomously within the hierarchical CRNN architecture. Finally, the proposed model is systematically evaluated through a comparative analysis with five representative baseline methods, encompassing both conventional machine learning algorithms and contemporary deep learning approaches. Experimental results demonstrate that the proposed CRNN framework achieves superior performance, achieving 98.2% classification accuracy, 97.2% sensitivity, 99.2% specificity, 97.4% recall, and 97.6% precision on the evaluated wearable-sensor datasets. Compared with existing methodologies, the proposed model consistently achieves higher recognition accuracy and greater generalization robustness, underscoring its efficacy for wearable-sensor-based activity recognition and its considerable potential for broader deployment in IoT-enabled monitoring paradigms and adaptable solutions in educational settings.

Sports monitoring and analysis have seen significant advancements by integrating cloud computing and continuum paradigms facilitated by machine learning and deep learning techniques. This study presents a novel approach for sports monitoring, specifically focusing on basketball, that seamlessly transitions from traditional cloud-based architectures to a continuum paradigm, enabling real-time analysis and insights into player performance and team dynamics. Leveraging machine learning and deep learning algorithms, our framework offers enhanced capabilities for player tracking, action recognition, and performance evaluation in various sports scenarios. The proposed Cloud-to-Thing continuum-based sports monitoring system utilizes advanced techniques such as Improved Mask R-CNN for pose estimation and a hybrid metaheuristic algorithm combined with a generative adversarial network (GAN) for classification. Our system significantly improves latency and accuracy, reducing latency to 5.1 ms and achieving an accuracy of 94.25%, which outperforms existing methods in the literature. These results highlight the system's ability to provide real-time, precise, and scalable sports monitoring, enabling immediate feedback for time-sensitive applications. This research has significantly improved real-time sports event analysis, contributing to improved player performance evaluation, enhanced team strategies, and informed tactical adjustments.

With the convenience brought by the rapid development of information, teaching reform has also set off a heat wave. The emergence of Internet+ , multimedia technology, virtual reality technology and artificial intelligence has made education continue to develop towards informatization, and mobile learning has become a way for students to learn. This paper provides a reference for the reform of exercise classes in colleges and universities, enriches the teaching methods, improves the learning quality of students, and makes the action image processing technology better applied to physical education classrooms. In this paper, according to the characteristics of the human motion system, the training actions of sports are collected. After the 3D action data is properly calibrated and normalized, the motion vector is used as the training input method. This method conforms to the laws of the human motion system. In terms of temporal feature extraction, this paper proposes a multi-level long short-term memory neural network structure for action recognition. The action image processing technology improves the accuracy of movements more significantly, and proves that it has a certain promotion effect on the self-editing ability and innovation ability of students'. The final results of the research show that the accuracy of action recognition has been improved to a certain extent after image data processing. The accuracy of action recognition in images has increased from 67.13% to 91.23%, and the accuracy rate has remained above 90% after action image processing which proves the effectiveness of the neural network algorithm processing method.

The main aim of this study was to investigate the effects of short burst speed and change-of -direction exercises on sprint and agility performance in youth female soccer players 15.5 (± 0.7) years. One training group (n=10) followed an eight-week, once per week training program consisting of short-burst sprinting exercises in addition to two normal soccer training, while a control group (n=9) followed three traditional soccer training session. Pre-and post-tests assessed significant improvement in 10 m sprint, pre 1.99 s (± 0.08), post 1.91 s (± 0.09), 20 m sprint pre 3.53 s (± 0.15), post 3.42 s (± 0.15), and agility performance, pre 8.23 s (± 0.31), post 7.80 s (± 0.33). Control group (15.1 years (± 0.7) did not demonstrate significant change in performance during the intervention period. The results demonstrate that a training program of short burst high speed exercises improve linear sprint and agility performance in youth female soccer players, beyond the gain of traditional soccer training consisting of small-sided games.

The accurate detection and recognition of human actions play a pivotal role in aerial surveillance, enabling the identification of potential threats and suspicious behavior. Several approaches have been presented to address this problem, but the limitation still remains in devising an accurate and robust solution. To this end, this paper presents an effective action recognition framework for aerial surveillance, employing the YOLOv8-Pose keypoints extraction algorithm and a customized sequential ConvLSTM (Convolutional Long Short-Term Memory) model for classifying the action. We performed a detailed experimental evaluation and comparison on the publicly available Drone Action dataset. The evaluation and comparison of the proposed framework with several existing approaches on the publicly available Drone Action dataset demonstrate its effectiveness, achieving a very encouraging performance. The overall accuracy of the framework on three provided dataset splits is 74%, 80%, and 70%, with a mean accuracy of 74.67%. Indeed, the proposed system effectively captures the spatial and temporal dynamics of human actions, providing a robust solution for aerial action recognition.

Action recognition methods based on human skeleton can explicitly represent human actions, and have gradually become one of the important research directions in the field of computer vision. To address the problems that the skeleton graph in graph convolutional networks is fixed to represent only the physical structure of the human body and the lack of adaptive ability to the skeleton topology graph structure, this paper proposes a dual-stream non-local graph convolutional network based on the attention mechanism. First, the temporal convolution layer is extended to a parallel structure with multiple kernels, and different temporal convolution kernel modules are adaptively selected to collect features by channel weights; second, an attention model consisting of an attention pooling layer is proposed to capture the correlation and temporal continuity among joints; finally, the nonlocal graph convolution network is used as the basic framework with joint information, skeletal information and respective motion information A dual-stream fusion model is constructed. The proposed method is compared with the mainstream methods in recent years on the action recognition dataset NTU RGB+D, and the experimental results show that the proposed method achieves high accuracy in action recognition.

Abstract: BACKGROUNDː Several studies have examined the effects of creatine supplementation in adult athletes in season or pre-season preparation. However, few studies have examined the effects of creatine supplementation in adolescent soccer players during reduced training in an off-season. OBJECTIVE: The aim of the study was to examine the effects of short-term creatine monohydrate supplementation on the anaerobic performance and body composition in adolescent soccer players during reduced training in an off-season. METHODSː Using a double-blind experiment design, 16 soccer players (aged 18.0 ± 0.8 yr) were randomly assigned to 5 days of either 20 g . day-1 creatine monohydrate (Cr) or placebo supplementation. One day before and a day after the supplementation, participants completed squat and countermovement jumps (SJ, CMJ), 10-m running sprint, 6-s single cycling sprint (CST), an intermittent anaerobic test on a bicycle ergometer (10 x 6s, IAnTBE) and measurement of body composition. RESULTSː Cr supplementation had no significant effect (p &gt; .05) on any performance test. However, effect size values indicated medium or small clinical significance in SJ (d = 0.59), CST (6-s power, d = 0.50; peak power, d = 0.48) and IAnTBE (best peak power, d = 0.44; post-exercise blood lactate concentration, d = - 0.59; fatigue index, d = - 0.28 ). Relative to the placebo, Cr supplementation resulted in a significant increase in body weight (BW) (p = .015). CONCLUSIONSː The results of the study suggest that short-term Cr supplementation administered to adolescent soccer players during their off-season significantly increase body weight and could have small/medium clinical significance effect on improve lower-body maximal anaerobic power output and power output recovery during maximal intermittent exercise. The study also confirms that Cr supplementation is safe and without side effects for adolescent athletes.

How to deal with multi-modality data from different types of devices is a challenging issue for accurate recognition of human activities in a smart environment. In this paper, we propose a multimodal fusion enabled ensemble approach. Firstly, useful features collected from Bluetooth beacons, binary sensors, and smart floor are extracted and presented by fuzzy logic based-method with variable-size temporal windows. Secondly, a group of support vector machine classifiers are used to perform the classification task. Finally, a weighted ensemble method is used to obtain the final prediction. Especially, by applying the geometric framework, we are able to obtain the optimal weights for the ensemble. The proposed approach is evaluated on the UJAmI dataset. The experimental results demonstrate the efficacy and robustness of the proposed method.