整合檢索增強生成與大型語言模型應用於精準運動科學平台：架構與實現;Integrating Retrieval-Augmented Generation and Large Language Models in a Precision Sports Science System: Architecture and Implementation

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题名:	整合檢索增強生成與大型語言模型應用於精準運動科學平台：架構與實現;Integrating Retrieval-Augmented Generation and Large Language Models in a Precision Sports Science System: Architecture and Implementation
作者:	丁郁庭;Ting, Yu-Ting
贡献者:	資訊工程學系
关键词:	運動科學;生成式 AI;大型語言模型(LLM);檢索增強生成(RAG);語意匹配;LangChain;Sports Science;Generative AI;Large Language models(LLM);Retrieval Enhancement Generation(RAG);Semantic Matching;LangChain
日期:	2024-07-30
上传时间:	2024-10-09 17:10:15 (UTC+8)
出版者:	國立中央大學
摘要:	精準運動科學平台收集和管理運動員的數據，為所有運動員提供高度機密和安全的數據存取空間。它支援高效搜尋和即時回饋，基於角色的存取控制和 18 種資料格式的導入，允許個性化訓練和調整。該平台也採用增強的檢索生成以及大型語言模型來增加資料相關性並產生更完整的句子。這確保了最合適的個人化健康建議和最合適的飲食調整得到完整的實施，同時資料的完整性和可用性受到加密和保護。跨領域整合、視覺化介面、預測分析、即時監控和人性化設計使教練和運動員能夠更好地理解和實施收集到的數據。這些數據和後續資訊可以有助於體重和身高、體脂率的觀察和調整，以及避免不健康的體重和體脂率。各領域運動員數據的收集和管理分為兩部分。外在負荷：透過收集運動員日常訓練、友誼賽、模擬比賽、正式比賽的數據來獲得負荷，包括體能測驗結果和學業成績。在個人特徵方面，每月收集運動員的體重、身高、體脂等生理數據，每週的食品補充劑數據和每月的運動數據。內部負荷：這部分自動收集運動員骨骼監測、生理測試、心血管測試和生物力學評估的數據。它審查和評估運動員的內部負荷。在健康問題部分，系統會自動收集運動員的健康數據。它在此收集的數據包括 LEAF 問卷和OSTRC 傷害記錄，都是每月一次。還收集有關運動員心理測試的數據，這些數據是由Garmin 設備收集的。將數據儲存在雲端共享資料庫和雲端共享平台中，研究人員可以隨時存取它們。精準運動科學平台可以分析和管理大量的運動數據，為運動員提供最佳化的訓練方案;Athlete’s data is collected and managed using the Precision Sports Science Platform, which provides all athletes with a highly confidential and secure data access space. It supports efficient search and instant feedback, being based on role-based access control and the import of 18 data formats, which allow personalized training and adjustment. The platform employs enhanced retrieval generation as well as large language models to increase data relevance and generate fuller sentences. This ensures that the most suitable personalized health advice and the most appropriate dietary adjustment are implemented, while the data are confidential and protected in terms of their integrity and availability. The cross-domain integration, a visual interface, predictive analysis, real-time monitoring, and user-friendly design enable coaches and athletes to better understand and implement the collected data. The data and the following information can then contribute to the weight and height, body fat-watching and adjustment, as well as avoiding an unhealthy weight and body fat percentage. The collection and management of athlete data in various fields is divided into two parts. External Load: To obtain the load, data is collected for athletes’ daily training, friendly games, simulations competitions, and official competitions, including physical fitness test results and academic performances. In personal characteristics, data are collected monthly on the athletes’ physiologies such as weight, height body fat, weekly data on food supplements and monthly data on exercise. Internal Load: This part of the section automatically collects data on athletes’ bone monitors, physiological tests, cardiovascular tests, and biomechanical evaluations. It reviews and evaluates the athlete’s internal load. In the health problems part, the system automatically collects data on the health of the athlete. The data it collects under this include the LEAF questionnaire and OSTRC injury records, both monthly. We also gather data on the athlete’s psychological tests under this are the data collected by the Garmin devices.These data are stored in a cloud-shared database and in a cloud-shared platform, and researchers can always get access to them. In such a way, the Precision Sports Science Platform can analyze and manage a large number of sports data and can provide sportsmen with the optimal training programs.
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