A138: Research on Deep Learning for Enhancing Sports Performance

The integration of Explainable Artificial intelligence (XAI) into sports training offers transformative capabilities for enhancing athlete performance, injury prevention, and personalized coaching. This paper presents the design and development of an intelligent sports training assistance system that leverages advanced AI technologies such as computer vision, deep learning, and wearable sensor analytics. The system incorporates real-time pose estimation using models like OpenPose and PoseNet, enabling precise motion tracking and automated technique correction across various sports disciplines including running, basketball, and martial arts. A multimodal dataset consisting of over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5, 0 0 0}$</tex> annotated video frames and sensor-based motion logs was used to train deep learning models including 3D Convolutional Neural Networks (3D-CNNs) and Long Short-Term Memory (LSTM) networks-a variant of Recurrent Neural Networks (RNNs) for motion classification, fatigue detection, and feedback generation. The system achieved 95.2 % pose estimation accuracy, 93.4 % movement classification accuracy, under 1.8s feedback latency, and a 40 % improvement in training efficiency compared to traditional coaching. The suggested framework is deployed in university training centers, the system demonstrated high reliability and responsiveness, offering real-time guidance, objective performance analytics, and remote coaching support, making it a scalable and intelligent solution for modern sports training and performance enhancement.

Millions of people worldwide suffer from Parkinson’s disease (PD), a neurodegenerative disorder marked by motor symptoms such as tremors, bradykinesia, and stiffness. Accurate early diagnosis is crucial for effective management and treatment. This article presents a novel review of Machine Learning (ML) and Deep Learning (DL) techniques for PD detection and progression monitoring, offering new perspectives by integrating diverse data sources. We examine the public datasets recently used in studies, including audio recordings, gait analysis, and medical imaging. We discuss the preprocessing methods applied, the state-of-the-art models utilized, and their performance. Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). These algorithms have shown promising results in PD diagnosis with accuracy rates exceeding 99% in some studies combining data sources. Our analysis particularly showcases the effectiveness of audio analysis in early symptom detection and gait analysis, including the Unified Parkinson’s Disease Rating Scale (UPDRS), in monitoring disease progression. Medical imaging, enhanced by DL techniques, has improved the identification of PD. The application of ML and DL in PD research offers significant potential for improving diagnostic accuracy. However, challenges like the need for large and diverse datasets, data privacy concerns, and data quality in healthcare remain. Additionally, developing explainable AI is crucial to ensure that clinicians can trust and understand ML and DL models. Our review highlights these key challenges that must be addressed to enhance the robustness and applicability of AI models in PD diagnosis, setting the groundwork for future research to overcome these obstacles.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

The use and knowledge of artificial intelligence (AI) in improving sports performance among male intervarsity handball players was the focus of this research, which examines the relationship between AI and sports. Data from 120 Pakistani intervarsity handball players was gathered using a survey questionnaire. The researcher utilized survey research design as part of a quantitative research strategy. The purpose of the research was to examine the association between AI awareness, usage and sports performance. Simple random sampling was used by the researcher. The researcher used a self-administrated questionnaire as a research tool. Pearson correlation analysis for relationship and SPSS 26, version for analysis of collected data was used by the researcher. The results show a strong positive relationship between enhanced sports performance and the use of AI technologies, including wearable technology. Furthermore, it was shown that athletes with higher levels of AI awareness performed better, indicating that a greater understanding of AI technology might improve sports performance. This study demonstrates how artificial intelligence (AI) has the potential to transform sports training and performance optimization, offering coaches, players, and sports organizations insightful information on how to use AI to achieve optimal performance in competitive sports.

Artificial intelligence (AI) has revolutionized sports science, advancing performance analysis, injury prevention, and strategy optimization. However, its long-term impact remains underexplored. This study conducts a bibliometric and predictive analysis of -driven sports research over the past decade, identifying key contributors, emerging trends, and future directions through visualization techniques. Method: A systematic review was conducted on related sports research from 2014 to 2024 using the Web of Science Core Collection. Bibliometric tools Citespace and Vosviewer were employed to analyze publication trends, author networks, institutional collaborations, and keyword co-occurrences. Polynomial regression analysis was applied to forecast future research growth based on historical publication and citation trends. A total of 5,811 publications with 96,753 citations were identified. China, the United States, and the United Kingdom were the most productive countries, with China leading in volume but exhibiting lower citation impact. The Chinese Academy of Sciences, Stanford University, and the University of Oxford were the top research institutions. Keyword analysis revealed that "machine learning," "deep learning," and "computer vision" were the most studied topics, while emerging themes such as "stress analysis," "information processing," and "pose estimation" indicated shifts towards driven real-time monitoring and predictive analytics. Polynomial regression models predicted continued research growth, with publication trends following y = 354x ² + 1350x + 528 (r ² = 0.94) and citation growth modeled as y = 9680x ² + 24900x + 7850 (r ² = 0.99), suggesting sustained acceleration in AI applications within sports science. The integration of sports science has grown rapidly, with machine learning and computer vision playing a pivotal role in optimizing athletic performance, real-time feedback, and injury prevention. Predictive analytics and driven modeling can transform sports training by personalizing programs based on biomechanical and physiological data, enhancing both performance and injury resilience. However, disparities in AI adoption across regions and institutions highlight the need for greater international collaboration, particularly in developing regions where access to driven sports technologies is limited. Future research should refine -driven models for individualized training, integrate wearable sensor data for precision, and address ethical concerns. Additionally, policymakers and sports organizations should invest in AI-based training and health monitoring systems to bridge the gap between technology-rich and technology-limited regions. As it evolves, its role in sports science will expand, driving advancements in performance analysis, health monitoring, and strategic decision-making.

Deep 3D human pose estimation: A review

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability.

Deep Learning and Beyond: AI’s New Horizons” is an expansive monograph that delves into the dynamic and rapidly evolving field of deep learning, a key area of artificial intelligence. This comprehensive work provides an insightful exploration of the revolutionary developments, foundational concepts, and future directions of deep learning, positioning itself as a crucial resource for understanding this transformative technology. The monograph commences with an introductory chapter that lays the groundwork by elucidating the background of machine learning and chronicling the ascent of deep learning. This foundational perspective is vital for comprehending the underlying principles and methodologies that have propelled deep learning to the forefront of technological innovation. Subsequent chapters delve into the “Historical Evolution” of AI, tracing the origins of neural networks from their early inception to the Renaissance period of deep learning. This historical analysis is pivotal in understanding the field’s trajectory and significant milestones. The “Foundations of Deep Learning” chapter presents an in-depth analysis of neural networks, covering their basic architecture, activation functions, layers, and the critical process of backpropagation and gradient descent. This is followed by examining “Advanced Neural Network Architectures,” where the focus shifts to state-of-the-art models such as convolutional neural networks, recurrent neural networks, and groundbreaking transformers and attention mechanisms. In addressing the “Training Deep Models,” the monograph explores regularization techniques, optimization strategies, and the increasingly significant role of transfer learning and pre-trained models. The application-oriented chapter, “Applications of Deep Learning,” showcases real-world implementations in domains like image recognition, natural language viii processing, and autonomous systems, illustrating deep learning technologies’ vast potential and versatility. The monograph also tackles the “Challenges in Deep Learning,” including overfitting, interpretability, and the ethical considerations surrounding AI deployment. It then ventures into the “Future of Deep Learning,” contemplating emerging trends such as integrating quantum computing, neural architecture search, and the convergence of neuroscience and deep learning. “Case Studies” provide practical insights and empirical evidence of the impact of deep learning. At the same time, the concluding chapter synthesizes the current state of deep learning, offers predictions for the future, and provides reflective thoughts on this transformative field. “Deep Learning and Beyond: AI’s New Horizons” educates and informs readers about the vast potential and future possibilities of deep learning and artificial intelligence. Keywords: Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Quantum Computing, Ethical AI, Neural Architecture Search, Natural Language Processing, Autonomous Systems, AI in Healthcare, Future of AI, AI Applications, AI Challenges.

This systematic literature review explores the emerging field of remote-based deep learning predictive algorithms for depression, focusing on addressing the limitations of traditional diagnostic methods and examining the current state of research in this novel area. A systematic search was conducted in Embase, Medline, Web of Science Core Collection, CINAHL, and PsycINFO in June 2023. To capture relevant studies, titles and abstracts of the papers were reviewed against predefined inclusion and exclusion criteria using four groups of keywords addressing prediction, depression, validity, and deep learning. Eligible studies were systematically reviewed based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment (PROBAST) Tool for methodological quality. The synthesis of data was conducted using the Synthesis Without Meta-Analysis (SWiM) framework. From 286 studies initially identified, 6 studies met all inclusion criteria, published between 2020 and 2023. Performance metrics revealed the potential of deep learning models, with accuracy rates reaching as high as 98.23%. Convolutional neural networks (CNNs) emerged as the predominant model, with applicability across diverse data sources such as speech recordings, body motion data, and facial images. However, issues related to risk of bias were prevalent, with most studies lacking essential reporting details and employing relatively small sample sizes. The review identified limitations in the practical application of these models, including limited demographic representation, absence of external validation, and a notable absence of models capable of anticipating the onset of depression. While the current models focus primarily on identifying existing depression of any duration, there is a need for advancements that enable the anticipation of future depressive episodes. To advance this field, we recommend standardized reporting practices, larger and more diverse datasets, external validation, and the development of predictive models that anticipate depression occurrences in advance. These enhancements will contribute to the credibility and real-world relevance of these models. While remote-based deep learning predictive algorithms hold promise in revolutionizing depression prediction, they require refinement and validation to fulfil their potential in clinical practice. This review underscores the need for further research and development in this area to address the identified limitations and contribute to improved mental health assessment and intervention.

In the field of construction, the safety of construction workers on construction sites has been a major problem over the years. According to statistic of previous study, approximately 88% of these accidents are caused by unsafe behaviors. In recent years, the development and application of deep learning have attracted considerable research interest such as object detection, human pose estimation, etc. Meanwhile, the object detection techniques have been applied to detect whether workers wear or use proper equipment. However, using proper equipment doesn't mean performing correct operation. Therefore, this paper proposes a deep learning framework, DeepSafty, to detect unsafe behaviors of construction workers through their postures to reduce their mortality rate. In our proposed DeepSafty, the object detection model YOLOv3 is used to locate construction workers precisely. Then, human pose estimation technology is used to determine various postures of construction workers. There are 17 joints in the poses. In other words, a 51-dimensional vector will be produced from the neural network, as unsafe behaviors are continual movements in the field of construction. Finally, a model that deals with time series in deep learning, i.e., long short-term memory, is used to solve any classification problems of time-dependent joint vectors. Finally, we conduct a comprehensive experimental study based on a dataset collected through a CCTV of a real construction site. The results demonstrate the effectiveness of our deep learning approach and show the strength of taking both object detection and human pose estimation into account in unsafe behaviors of construction workers.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

&lt;p&gt; Background: The application of Unmanned Aerial Vehicle (UAV) is a major turning point in the history of power line inspection and is of increasing interest to a growing number of researchers. &lt;/p&gt;&lt;p&gt; Objective: This study aimed to clarify the research status, hotspots, and evolutionary trends of UAV power line inspection, to help researchers understand the dynamic evolution of research topics and provide guidance for future research directions. &lt;/p&gt;&lt;p&gt; Methods: 737 high-quality papers were collected from the Web of Science Core Collection (WoSCC) database from 2001 to 2023, and descriptive statistical analysis, cooperation network analysis, keyword co-occurrence analysis, keyword clustering analysis, and keyword citation burst analysis were conducted using VOSviewer and CiteSpace. &lt;/p&gt;&lt;p&gt; Results: The popularity of research in this field is increasing. Chinese academic institutions and scholars have made outstanding contributions. The level of cooperation between different countries, academic institutions, and scholars is low. The research on UAV power line inspection has a wide range of topics and obvious interdisciplinary characteristics, mainly focusing on fault detection and diagnosis, path planning, and the application of deep learning in intelligent inspection. The research hotspot and trend is the integration of artificial intelligence, deep learning, and modern information technology to achieve UAV autonomous intelligent inspection. Some of the challenges faced in the development of the field are also summarized and possible solutions are proposed. &lt;/p&gt;&lt;p&gt; Conclusion: This study provides a comprehensive and systematic review, and the results can provide a quick overview of the research status, hotspots, and evolutionary trends.

Deep Learning Applications in Primary Education: A Systematic Literature Review of Emerging Trends, Challenges, and Opportunities. Objectives: Despite significant advances in healthcare and finance, Deep Learning (DL) remains underutilized in primary education, with only about 12% of AI-related studies focusing on K–6. Given the formative role of early schooling in cognitive and social development, this systematic review analyzed recent empirical studies to identify key trends, challenges, and opportunities in applying DL to primary education. Methods: Following the PRISMA 2020 guidelines, 21 studies published between 2021 and 2025 across 11 countries were analyzed using a structured coding sheet and further examined with bibliometric mapping, Microsoft Excel, and Python-based visualization tools. The review included diverse sources focusing on DL use cases across global primary education systems. Findings: The findings reveal a global increase in DL adoption, led by China and South Korea, with growing contributions from Indonesia, India, and the Philippines. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer-based models are among the most commonly applied architectures to tasks such as personalized learning, early detection of learning difficulties, assessment automation, and curriculum development. However, there are still problems in the context of ethical concerns, including data privacy, algorithmic bias, and equity of access. Technical barriers involve dataset complexity, model generalization, and resource limitations, while pedagogical issues center on aligning DL applications with developmental needs and classroom realities. Despite these obstacles, DL demonstrates significant potential to enhance personalization, foster engagement, and support holistic educational outcomes. Conclusion: This review contributes a strategic roadmap for integrating DL in primary education by balancing innovation with pedagogy and ethics. Future research should prioritize cross-disciplinary collaboration, greater geographic diversity, and improvements in scalability and interpretability to ensure DL supports equitable, future-ready learning. Keywords: deep learning, primary education, personalized learning, systematic literature review, educational technology.

As societies age, the number of individuals experiencing stroke increases, necessitating more effective rehabilitation strategies. Over half of stroke survivors suffer from upper limb impairments, making assessments of sensory-motor function crucial for both improving interventions and tracking progress. Ideally, such assessments could also be performed at home without requiring a therapist's presence. Advances in computer vision and human pose estimation allow for human movement analysis using consumer-grade cameras. This study investigates whether a single webcam, combined with human pose estimation and deep learning algorithms, can automatically detect compensatory movements in persons with stroke performing a drinking task. Twenty participants with stroke with mild to moderate upper limb impairment were recruited. Each participant performed multiple repetitions of the drinking task while being recorded by multiple cameras and an optical motion capture system (OMC) for kinematic ground truth. The videos were labeled by therapists to indicate the presence or absence of compensatory movements. Human poses were extracted from the videos using MediaPipe, and deep learning models were trained to predict these compensatory movements based on MediaPipe keypoints. Several factors affecting compensation detection accuracy were evaluated. Models trained on raw MediaPipe keypoints for inter-person compensation detection failed to generalize, achieving accuracy around 50%. Using custom features instead of raw keypoints improved the accuracy to 70%. In contrast, intraperson classification achieved high accuracy, typically exceeding 90%. Using OMC data significantly improved classification accuracy compared to using MediaPipe keypoints. Camera angle had an effect on accuracy, and convolutional neural networks outperformed long short-term memory networks. Generalizing models remain limited by (1) the measurement uncertainty of human pose estimation and (2) insufficient data representing the full spectrum of compensatory strategies (3) accurate compensation labels. The results demonstrate that deep learning approaches can differentiate between compensatory and non-compensatory movements when movement representations are sufficiently accurate. Future work should improve pose estimation and expand labeled datasets to better reflect the stroke population. While general models are limited in accuracy, personalized models using consumer cameras can support home-based rehabilitation. This digitalized assessment approach has the potential to quantify recovery progress throughout the continuum of care.