研究生: |
李凱金 Lei, Hoi-Kam |
---|---|
論文名稱: |
數據驅動的球隊管理: 以 IoT 感測器提升桌球表現的實驗 Data-Driven Team Management: An Experimental Study on Enhancing Table Tennis Performance Using IoT Sensors |
指導教授: |
林世昌
Lin, Shih-Chang |
口試委員: |
周大森
曾雅雯 |
學位類別: |
碩士 Master |
系所名稱: |
科技管理學院 - 經營管理碩士在職專班 Business Administration |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 45 |
中文關鍵詞: | 物聯網 、乒乓球 、桌球 、慣性感測器 、加速規 、爆發力 |
外文關鍵詞: | Inertial Sensor |
相關次數: | 點閱:54 下載:0 |
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本研究旨在透過探索物聯網 (IoT) 慣性感測器技術應用在提升乒乓球中提拉下旋球的中的數據分析,以此數據驅動技術對球隊管理和訓練方法的革新為重點,從傳統的經驗傳承和主觀感受的訓練方式轉為數據量化,結果可視化的訓練方式。
研究的動機源於在清華大學桌球社觀察到,初學者在執行提拉下旋球動作時常遭遇挑戰。鑒於此,本研究在分析提拉下旋球高成功率與低成功率的球員在揮拍感測器收集的數據上的差異,並通過數據驅動的方法提升進球率,達到更有效的訓練效率。
研究採用 IoT 慣性感測器,涵蓋 X、 Y、 Z 軸的加速度、角速度和軸轉角度,來偵測揮拍時的各項參數。通過分析這些參數與拉球成功率之間的相關性,研究揭示能量轉換、擊球速度、擊球角度、以及旋轉等因素對乒乓球回球速度和方向的影響。此外,球員的年紀、球齡、爆發力、是否曾被訓練等因素對擊球成功率的影響也被考量在內。
主要的研究發現顯示,揮拍加速度、角速度、球齡、爆發力和教練的介入,對於拉球成功率有顯著影響。數據分析結果不僅揭示高成功率進球和低成功率揮拍之間的差異,也為教練和球員提供具體的數據支持,以優化訓練計劃和目標,甚至對於球隊的未來球員的徵選提供量化結果。這項研究表明,利用 IoT 慣性感測器進行的數據分析,能有效提升乒乓球運動員的表現,並對球隊管理和訓練方法帶來革命性的改變。
This study aims to explore the application of Internet of Things (IoT) inertial sensor
technology in enhancing the data analysis of the forehand spin against cut defence in table
tennis. The focus is on the innovation of team management and training methods driven by
data, transitioning from traditional experience inheritance and subjective training methods
to data quantification and visualized results for training.
The motivation for the research originated from the observation in the Tsinghua University Table Tennis Club that beginners often encounter challenges when executing the
forehand spin against cut defence. In light of this, the study analyzes the differences in
sensor-collected data between players with high and low success rates in performing this
stroke and seeks to improve the rate of successful shots through a data-driven approach,
leading to more effective training efficiency.
The study employed IoT inertial sensors, covering the acceleration, angular velocity, and axis rotation angle on the X, Y, and Z axes, to detect various parameters during the stroke. By analyzing the correlation between these parameters and the success rate of spin shots, the research reveals the impact of energy transfer, striking speed, striking angle, and
spin on the speed and direction of table tennis returns. Furthermore, factors such as the player’s age, years of experience, explosive power, and prior training were also considered in their influence on the success rate of shots.
Key findings indicate that the acceleration and angular velocity of the stroke, years of playing, explosive power, and the involvement of a coach significantly impact the success rate of the forehand spin against cut defence. The data analysis not only reveals the differences between high success rate shots and low success rate strokes but also provides
concrete data support for coaches and players to optimize training plans and objectives, and even offers quantified results for future player selection of the team. This research demonstrates that data analysis conducted with IoT inertial sensors can effectively enhance the performance of table tennis athletes and bring revolutionary changes to team management and training methods.
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