An Intelligent Mobile Application to Improve Users’ Shooting Motion and Compare Them with NBA

Описание: An Intelligent Mobile Application to Improve Users’ Shooting Motion and Compare Them with NBA Players using Machine Learning and Large Language Models

Yu Chu (Nanchang University, China) and Garret Washburn (California State Polytechnic University, USA)

Abstract

Basketball is one of the most popular sports worldwide, with over 610 million people aged 6 to 54 playing the game at least twice a month, according to FIBA. However, access to systematic and professional basketball training remains limited, especially in developing countries, where only 1%–3% of players may receive professional coaching. This lack of access makes it difficult for most basketball enthusiasts to learn and refine proper shooting techniques. To address this issue, we propose Sharp Shooter—a mobile application that helps users improve their shooting form without requiring professional training or expensive equipment. Our solution combines several cutting-edge technologies: MediaPipe is used to extract key body landmarks from users' uploaded shooting videos; these landmarks are then analyzed by a large language model (LLM) to provide expert-level feedback [10]. Additionally, the app matches users’ shooting forms with those of professional NBA players stored in a custom database, allowing users to see which NBA player their form most resembles—further enhancing engagement and motivation

Keywords

MediaPipe, Random Forest, Mobile Application, Shooting Motion, Large Language Model

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#mediapipe #randomforest #mobileapplication #largelanguagemodel #machinelearning #naturallanguageprocessing