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動作分析において分節化は重要な役割を果たす特異スペクトル変換により筋力変化だけでなく周辺環境など外力変化によっても発生した動作変化点を抽出できるこのため動作分析において特異スペクトル変換は有効な手段であるが抽出した動作変化点列を直接分析することは困難であった本稿では動作変化点列の非類似度を定義するその非類似度に基づいて動作変化点列を低次元空間へ布置することにより動作変化点列の関係性を可視化できるさらに低次元空間における投影点に対して感度解析を用いることにより動作変化点列の変化を抽出する方法を提案する歩行動作の分析に適用した結果により提案手法の有効性を示す.Segmentation plays an important role in behavior analysis. Singular Spectrum Transformation (SST) can extract change-points of human's motion caused not only by changes of muscle forces, but also by those of external forces, and it is effective to transform human's motion data into motion change-points sequences. However, it is difficult to analyze change-points sequences to extract motion pattern changes directly from extracted sequences. In this paper, dissimilarity between two arrays of change-points is defined and a method to visualize the similarity of multiple arrays of change-points is described. Moreover, a method to extract the differences of change-points between two classes of change-points sequences based on sensitivity analysis is proposed. The proposed method was applied to analyze walking motion of different walking speed or different identities. The results prove the effectiveness of the proposed method

A Method for Behavior Analysis using Change-points Sequences Extracted by Singular Spectrum Transformation