研究生: |
潘柏遠 Po-Yuan Pan |
---|---|
論文名稱: |
從七手八腳到六步排腿—街舞排腿學習的量化與分析 Quantitative analysis on learning the six-step footwork |
指導教授: |
劉有德
Liu, Yeou-Teh |
學位類別: |
碩士 Master |
系所名稱: |
運動競技學系 Department of Athletic Performance |
論文出版年: | 2014 |
畢業學年度: | 102 |
語文別: | 中文 |
論文頁數: | 54 |
中文關鍵詞: | 六步排腿 、主成份分析 、協調 |
英文關鍵詞: | Six Step Footwork, Principal Component Analysis, Movement Coordination |
論文種類: | 學術論文 |
相關次數: | 點閱:194 下載:6 |
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美國傳奇街舞舞者Alien Ness曾經說過"Footwork is the original Power move." ( Martinez, 2013)。他認為排腿相當具有力量,甚至覺得排腿才能夠算是power moves。排腿的種類繁多,但如何才能做出人人讚賞的排腿呢? 許多舞者都認為在執行排腿工作時,掃腿(sweep)為最能辨別排腿程度差異的變項,一位好的舞者有快速且大弧度的掃腿動作。70年代後期B-boy Spy將六步排腿訂為Footwork的基礎動作,至今,六步排腿一直被認為是Footwork的基礎。人體的運動本身就相當複雜多變,舞蹈則是更為複雜的動作,面對排腿複雜的動作控制,該如何有效運用肢段協調完成排腿工作呢?由於多肢段的全身性動作複雜度較高,不易分析,相較於單肢段的動作分析,相關文獻較少。近年來主成份分析法(principal component analysis, PCA) ,被有效運用於分析人體多肢段的協調動作,以資料縮減的方式,方便資料分析。目的:以主成份分析及其他運動學表現資料觀察、量化街舞中的六步排腿學習。方法:以6名沒有街舞經驗男性自願者為研究對象,利用IGS-190動作擷取系統收集參與者學習六步排腿動作資料進行分析。結果:6名參與者經5回練習後掃腿的各項表現及節奏準確性上都有明顯的進步。在PCA的成份數,累積變異量上個別的差異很大。成份在肢段上的分布經過練習後趨於穩定。結論: 前掃腿動作在練習的第二階段即有明顯進步,後掃腿則在第三階段才有明顯進步;而觀察參與者練習六步排腿於前、中、後三個階段協調型態的變化,發現在經過學習後主成份的數量、解釋變異在個體間有所差異,會有所增加、減少或是維持不變,先前研究中有經驗的舞者所呈現出的「繞圈」、及「六步」的協調,在本實驗經過練習後亦可觀察到。
The American legendary dancer Alien Ness has said" Footwork is the original Power moves" (Martinez, 2013). A strong footwork is even more powerful than power moves. However, how to perform an admirable footwork? The sweep is considered as the most discriminative movement in the footwork. In the late seventies, B-boy Spy set the six-step footwork as a basic move of the Breaking. To this day, six step footwork is considered the most important and fundamental move of footwork. Human movement coordination is complex and dance is even more complicated. There are relatively fewer studies about the whole body movement in the literature partly because it is hard to analyze. In order to effectively examine the coordination of the bodily segments, the reduction of the number of segments that are involved in the movement would help to simplify the task. Principal Component Analysis (PCA) is a technique for simplifying a dataset by reducing the multi-dimensional datasets to lower dimensions for analysis. Purpose: Using the performance outcome and the Principal Component Analysis to examine the change of coordination patterns in six-step learning. Method: Six participants with no hip-hop dance experience participated in this study. The IGS 190 motion digitizing system (120Hz) was used to capture the movement kinematics for PCA. Results: After 3 sessions of practice, every participant significantly improved in the sweeps and the accuracy of the tempo. Although there were great individual differences observed in the number of Principle Component (PC) and the variance accounted for among the participants, the participants have developed the stable patterns after 3 sessions of practice. Conclusions: The forward sweep was significantly improved at the 2nd session while the backward sweep was not improved until the 3rd session. The participants showed great individual differences in the process of developing the movement patterns. The periodic cycling patterns and the six stepping patterns that were observed in the expert performance of earlier study were also found in this study.
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