加速度時系列データからの反復作業パターンの教師なし発見方式

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: 2021/09/01

タイトル(英語): Unsupervised Discovery of Repetitive Activity Patterns in Acceleration Time Series Data

著者名: 寺田　昌弘（東海大学情報通信学部），皆川　拓海（東海大学情報通信学部），安齋　博人（東海大学情報通信学部），今村　誠（東海大学情報通信学部）

著者名(英語): Masahiro Terada (School of Information and Telecommunication Engineering, Tokai University), Takumi Minakawa (School of Information and Telecommunication Engineering, Tokai University), Hiroto Anzai (School of Information and Telecommunication Engineering

キーワード: モチーフ，モーションセンサ，行動認識，文法的推論，生産性向上　　motif，motion sensor，activity recognition，grammatical inference，improve productivity

要約(英語): In order to automatically analyze human motion data, it is important to extract basic actions such as “stand up” and “walking”. The most conventional methods are several issues: (1) they need labeled data, (2) they only classify data but not extract the structure of data, (3) they require a window size of time series in advance. This paper proposes a novel unsupervised pattern discovery method which extracts a series of repetitive actions by recognizing basic actions in time series. The proposed method consists of a basic action enumeration process and a grammatical inference process. The former discovers the subsequence of basic actions using the Motif discovery method. The latter discovers grammatical patterns constructed with the sequence of the basic actions. Furthermore, we evaluate the accuracy of extracted basic actions and the duration time of a series of actions for packing operations and screw-tightening operations.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.141 No.9 （2021） 特集：知能メカトロニクス分野と連携する知覚情報技術

本誌掲載ページ: 1039-1047 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/141/9/141_1039/_article/-char/ja/