KhelQuest: AI-Powered Platform for Democratizing Sports

Abstract

Access to structured sports training and talent identification opportunities remains limited for many athletes due to geographical, economic, and infrastructural barriers. This paper presents KhelQuest, an Artificial Intelligence (AI)-powered mobile platform designed to enable accessible, objective sports talent assessment through smartphone-based video analysis. The proposed system integrates computer vision, pose estimation, machine learning, and cloud-based analytics to evaluate athletic performance without specialised equipment or professional evaluation centres. Athletes perform predefined fitness activities that are recorded using mobile devices, after which movement features are extracted and analysed to generate performance scores, rankings, and feedback. The platform also provides secure data storage, progress tracking, and comparative benchmarking across age groups and regions. Experimental evaluation demonstrates that the system can successfully process smartphone-recorded videos and produce consistent performance assessments, thereby reducing reliance on subjective judgment. The generated rankings and analytics support data-driven decision-making for athletes, coaches, and sports administrators. By combining lightweight Artificial Intelligence models with mobile computing and cloud infrastructure, KhelQuest offers a scalable, transparent, and cost-effective solution for grassroots sports development. The study concludes that smartphone-based Artificial Intelligence systems can significantly improve the accessibility, fairness, and efficiency of sports talent identification and performance monitoring, thereby contributing to a more inclusive and technology-enabled sports ecosystem.