拉邦動作分析(Laban Movement Analysis)是一套在舞蹈界行之有年、享譽盛名的動作分析系統,由舞蹈家魯道夫•拉邦所提出,可將多變的人體動作以系統化的方式來進行描述,其中的動作質地(Effort)論述描述了身體與空間、時間、力度的關係,可以用來分析動作者出力時的內在態度。 透過拉邦動作分析的專家建議,可幫助舞者或演員達到更好的表演效果,此學理也已經長期被使用在運動學、復健、兒童教育等專業領域,補足該領域對於人體動作觀察與分析的不足。而人體動作的質地內涵若能透過知識化與數位化的分析方法來進行量化的描述、進而建構分析診斷模型的話,勢必對目前需要人體動作分析的健康運動、休閒娛樂或動畫遊戲產業以及未來情緒傳達相關應用有所助益。 因此,本研究以動作擷取系統,將具備動作質地的人體基本動作紀錄於動作檔案中,並透過計算運動學參數及分析其動作波形,對拉邦動作質地(Effort)中時間(Time)元素的突然(Sudden)與綿延(Sustain)兩個因子、空間(Space)元素的直接(Direct)與迂迴(Indirect)兩個因子、與力度(Weight)元素的強力(Strong)與輕飄(Light)兩個因子進行量化分析,以求得動作質地的動作特徵值,而分析出的量化動作特徵值再以變異數分析來找出質地與動作特徵值之關聯,以決定哪些特徵值可用以辨認哪些動作質地。 研究結果顯示可利用本研究所設計與分析出的位移波形起伏數、累積位移、路徑與位移比、路徑波形週期性、動作時間、速率波形起伏次數、最大瞬時速率、平均速率、最大瞬時速率與平均速率比、最大瞬時速率與速率標準差比、角速度、累積角度變化量等動作特徵值來判斷人體的動作質地。最後本研究設計出一動作質地診斷規則,並以此規則來建立動作質地診斷模型,以達到自動化診斷動作質地的目的。
Laban Movement Analysis (LMA) is a well-known movement analysis system in dance field which is proposed by Rudolf Laban. It can describe various human movements in a systematic way. One theory of analysis in LMA is called Effort, which describes not only the body relationship with Space, Time, and Weight elements but also the movement intention in human action. By using Laban Movement Analysis theory, movement analysis experts can make suggestion to dancers for a better performance. The theory is also being applied in Kinematics, Rehabilitation, and Children Education fields to make up the deficiency of their way to observe and analyze human movement. If the Efforts of human movement can be quantified with a digitize way then establish an analysis and diagnosis model, it will be useful to health related exercise, entertainment, and 3D animation industries which need to analyze human movement. For this reason, this research record human motion data which contain Efforts in motion files by using Motion Capture System and use a kinematics and motion waveform analysis way to analyze Space, Time, and Weight elements in Efforts. This research will aim to quantify Sudden and Sustain factors in Time element, Direct and Indirect factors in Space element, and Strong and Light factors in Weight element to find out motion eigenvalues. After finding out motion eigenvalues, we use ANOVA (analysis of variance) to determine which eigenvalues can be used to recognize Efforts. The result of research shows that we can use motion eigenvalues which were designed by this research to recognize Efforts.Finally, this research design a set of Effort diagnosis rules and establish an Effort analysis and diagnosis model for the purpose of automatically diagnose Efforts.