Cloud-based big data for Wearable Technology using Adaptive Wearable Analytics Framework for Sports Education

Jayalakshmi V; J. Jebamalar Tamilselvi; Savitha G; Ms. Jenifer V; Ms P. Preethi

doi:10.5935/jetia.v12i61.3790

Authors

Jayalakshmi V Assistant Professor, Department of Cyber Security, SRM Institute of Science and Technology (FSH), Ramapuram, Chennai, Tamilnadu, India.
J. Jebamalar Tamilselvi Assistant Professor, Department of Cyber Security, SRM Institute of Science and Technology (FSH), Ramapuram, Chennai, Tamilnadu, India
Savitha G Assistant Professor, Department of Cyber Security, SRM Institute of Science and Technology (FSH), Ramapuram, Chennai, Tamilnadu, India
Ms. Jenifer V Assistant Professor, Department of Cyber Security, SRM Institute of Science and Technology (FSH), Ramapuram, Chennai, Tamilnadu, India
Ms P. Preethi Assistant Professor Department of CSE (Artificial intelligence and Machine Learning), Sri Eshwar College of Engineering, Kondampatti, Coimbatore 641202

DOI:

https://doi.org/10.5935/jetia.v12i61.3790

Abstract

The integration of wearable technology with big data analytics offers transformative potential for college sports teaching, enabling precise performance monitoring and tailored training plans. Traditional sports teaching methods depend heavily on manual observation, leading to inconsistent feedback and limited personalization. To address these challenges, this paper proposes the Adaptive Wearable Analytics Framework for Sports Education (AWAF-SE). AWAF-SE integrates wearable sensors, cloud-based big data analytics, and AI-driven teaching adaptation. The framework collects real-time physiological and biomechanical data, processes it using machine learning algorithms, and delivers personalized training recommendations and performance feedback to both students and instructors. Pilot implementation of AWAF-SE in collegiate sports programs demonstrated a 28% improvement in training efficiency, a 22% increase in skill acquisition rates, and enhanced student engagement compared to traditional teaching methods. Cloud assisted Data driven insights reduced instructor workload while enabling more precise performance evaluations. The results indicate that AWAF-SE provides a scalable, Cloud assisted Data driven solution for revolutionizing sports education. By integrating wearable technology and adaptive analytics, AWAF-SE bridges the gap between technology and pedagogy, creating an innovative and sustainable model for future sports teaching.