An approach to categorization analysis for human motion by Kinect and IMU

研究成果: Conference contribution

2 引用 (Scopus)

抜粋

The field of human motion analysis has researched with designing the rehabilitation robot system. In the present, there are many robot systems dealing with the human motion but they are heavy and hard to use simply at home. Moreover they especially have a particular purpose focused on the single human motion then the usability of these systems is low and fragmentary about a variety of motions. To classify the various motions, we propose the approach to categorization analysis for human motion by using information of the Kinect and IMUs. It is focused on the human motion which is classified into the walking, standing up and down, and falling down motion. The two COG points of the body from the sensing results are defined as the new indexes such as distance and incline, which are the indicator to classify the human motion. To verify the verification, the theoretical human model is designed and the experiment for the various human motions is carried out by using Kinect and IMU.

元の言語English
ホスト出版物のタイトルProceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society
出版者IEEE Computer Society
ページ6158-6162
ページ数5
ISBN(電子版)9781509034741
DOI
出版物ステータスPublished - 2016 12 21
イベント42nd Conference of the Industrial Electronics Society, IECON 2016 - Florence, Italy
継続期間: 2016 10 242016 10 27

Other

Other42nd Conference of the Industrial Electronics Society, IECON 2016
Italy
Florence
期間16/10/2416/10/27

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

フィンガープリント An approach to categorization analysis for human motion by Kinect and IMU' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Kim, S., Nozaki, T., & Murakami, T. (2016). An approach to categorization analysis for human motion by Kinect and IMU. : Proceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society (pp. 6158-6162). [7793391] IEEE Computer Society. https://doi.org/10.1109/IECON.2016.7793391