1A2-D17 Behavior Labeling Algorithms from Accumulated Sensor Data matched to Usage of Livelihood Support Application

• Oshima K.

  The University of Tokyo

• Urushibata R.

  The University of Tokyo

• Fujii A.

  The University of Tokyo

• Noguchi H.

  The University of Tokyo

• Shimosaka M.

  The University of Tokyo

• Sato T.

  The University of Tokyo

• Mori T.

  The University of Tokyo

Bibliographic Information

Other Title

• 1A2-D17 蓄積センサデータを用いた生活支援の用途に合わせた行動ラベリング手法

Abstract

This paper presents three behavior labeling algorithms based on supervised learning using accumulated pyroelectric sensor data in the living space. We summarize features of each algorithm to use them in combination matched to usage of the livelihood support application. They are (a)labeling algorithms based on switching model around a behavioral change-point, (b)one based on time attribution of "on-off" data, and (c)one based on Hidden Markov Models. We show the behavior labeling results of three algorithms for one month data under the same conditions. We summarize features on the basis of these results.

Journal

• The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)

  The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2009 (0), _1A2-D17_1-_1A2-D17_4, 2009

  The Japan Society of Mechanical Engineers

Keywords

• behavior labeling
• switching model
• Gaussian mixture models
• hidden Markov models
• pyroelectric sensor

Details

• CRID: 1390001205935232000
• NII Article ID: 110008698000
• DOI: 10.1299/jsmermd.2009._1a2-d17_1
• ISSN: 24243124
• Web Site: https://www.jstage.jst.go.jp/article/jsmermd/2009/0/2009__1A2-D17_1/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed