本論文基於具備捕捉影像深度的RGBD影音串流裝置－Kinect感測器，在簡報場域中，作為擷取簡報者肢體動作、表情、以及語言表現模式的設備。首先我們提出在特定時段內的表現模式，可以經由大眾的評估，而具有喜歡／不喜歡的性質，我們將其分別命名為Period of Like(POL)以及Period of Dislike(POD)。論文中並以三種Kinect SDK所提供的影像特徵：動畫單元、骨架關節點、以及3D臉部頂點，輔以35位評估者所提供之評估資料，以POD/POL取出的特徵模式，分析是否具有一致性，以及是否可用於未來預測。最後將研究結果開發應用於原型程式，期許這樣的預測系統，能夠為在簡報中表現不佳而困擾的人們，提點其優劣之處，以作為後續改善之依據。 / In this thesis, we capture body movements, facial expressions, and voice data of subjects in the presentation scenario using RGBD-capable Kinect sensor. The acquired videos were accessed by a group of reviewers to indicate their preferences/aversions to the presentation style. We denote the two classes of ruling as Period of Like (POL) and Period of Dislike (POD), respectively. We then employ three types of image features, namely, animation units (AU), skeletal joints, and 3D face vertices to analyze the consistency of the evaluation result, as well as the ability to classify unseen footage based on the training data supplied by 35 evaluators. Finally, we develop a prototype program to help users to identify their strength/weakness during their presentation so that they can improve their skills accordingly.