人間行動の統計的モデリングのための遅れを伴う状況-行動間の因果関係パターン抽出手法

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2015/01/01

タイトル(英語): An Extraction Method of Causality Patterns between Situation and Human Action with Delay Time for Statistical Modeling of Human Action

著者名: 橋本　幸二郎（名古屋大学大学院工学研究科），道木　加絵（愛知工業大学），道木　慎二（名古屋大学大学院工学研究科）

著者名(英語): Kohjiro Hashimoto (Nagoya University), Kae Doki (Aichi Institute of Technology), Shinji Doki (Nagoya University)

キーワード: 人間の行動モデリング，時系列信号処理，人間機械系，時系列相関マイニング　　Human action modeling，Time series signal processing，Man-Machine system，Time series association mining

要約(英語): A modeling approach of human actions is focused, which designs human action model based on the obtained stored data during long-term monitoring of a person. This approach consists of the following two processes. At first, several kinds of frequent partial time series data are extracted from the stored data and regarded as human action patterns. Next, the extracted time series data are modeled based on a statistical modeling method such as Hidden Markov Model. In this research, it is focused on the extraction method of the frequent time series data in the stored data. A person changes his action according to the change of the situation around him. And, it takes some time for him to perform his action after he recognizes the situation around himself. This time is called delay time in this paper. A human action model considering this delay time leads to greater accuracy in recognition and prediction of human action based on the one. It is necessary to extract time series data of situation and action containing the delay time as learning data in order to generate the above human action model. In conventional methods, multi-dimensional time series data are used as the stored data without distinction between situation and action data. And, some frequent partial time series data are extracted from the stored data. Therefore, the delay time is not considered. In this paper, we propose a new extraction method of a frequent time series data considering the delay time. In this method, the frequent partial time series data with the delay time are extracted by evaluating the repeatability and relativity between the partial time series data with different occurrence time. In the experiment, extraction of a frequent interaction motion between two examinees is executed. The usefulness of the propose method is examined through some experimental results.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.1 （2015） 特集：インタフェース関連アナログ電子回路技術

本誌掲載ページ: 90-101 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/1/135_90/_article/-char/ja/