Cloud to Thing Continuum based Sports Monitoring System using Machine Learning and Deep Learning Model

Sports monitoring and analysis have seen significant advancements with the integration of cloud computing and continuum paradigms, facilitated by machine learning and deep learning techniques. In this study, we present a novel approach for sports monitoring 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. This research proposes a Cloud-to-Thing Continuum based Sports Monitoring System utilizing Machine Learning (ML) and Deep Learning (DL) models. The system integrates data acquisition, preprocessing, feature extraction, cloud-based processing, continuum paradigm integration, and decision-making stages. It leverages innovative techniques such as Improved Mask R-CNN for pose estimation, hybrid metaheuristic algorithms with Generative Adversarial Network (GAN) for classification, and fuzzy decision-making Based on the integrated analysis, decisions are made regarding player performance, team strategies, and tactical adjustments. The continuum approach ensures a balance between centralized cloud processing and distributed edge processing, optimizing resource utilization and reducing latency. Through this system, real-time analysis of sports events is achieved, enabling immediate feedback for time-sensitive applications.

随着云计算与连续体范式（continuum paradigms）的融合，并借助机器学习与深度学习技术的赋能，运动监测与分析领域已实现显著发展。本研究提出一种全新的运动监测方案，可从传统的云端架构无缝迁移至连续体范式，支持对运动员表现与团队协作态势的实时分析与洞察。本框架依托机器学习（Machine Learning, ML）与深度学习（Deep Learning, DL）算法，可在各类运动场景中实现更出色的运动员追踪、动作识别与表现评估功能。本研究提出一种基于云到物连续体（Cloud-to-Thing Continuum）的运动监测系统，采用机器学习与深度学习模型。该系统涵盖数据采集、预处理、特征提取、云端处理、连续体范式集成与决策制定等多个环节。该系统采用多项创新技术，例如用于姿态估计的改进型Mask R-CNN、结合生成对抗网络（Generative Adversarial Network, GAN）的混合元启发式算法，以及模糊决策方法。基于综合分析结果，系统可针对运动员表现、团队战术与战术调整制定相应决策。该连续体方案可实现集中式云端处理与分布式边缘处理的平衡，优化资源利用率并降低延迟。借助该系统，可实现运动赛事的实时分析，为时延敏感型应用提供即时反馈。