Cloud-to-Thing continuum-based sports monitoring system using machine learning and deep learning model.

ABSTRACT: 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 estimat

SUBMITTER: Alshardan A 

PROVIDER: S-EPMC11888854 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature