From Virtual to Real World: Applying Animation to Design the Activity Recognition System

Chengshuo Xia, Yuta Sugiura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Following the conventional pipeline, the training dataset of a human activity recognition system relies on the detection of the significant signal variation regions. Such position-specific classifiers provide less flexibility for users to alter the sensor positions. In this paper, we proposed to employ the simulated sensor to generate the corresponding signal from human motion animation as the dataset. Visualizing the corresponding items from the real world, the user can determine the sensor's placement arbitrarily and obtain accuracy feedback as well as the classifier interface to get relief from the cost of a conventional training model. With the cases validation, the classifier trained by simulated sensor data can effectively recognize the real-world activity.

Original languageEnglish
Title of host publicationExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI EA 2021
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450380959
DOIs
Publication statusPublished - 2021 May 8
Event2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021 - Virtual, Online, Japan
Duration: 2021 May 82021 May 13

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021
Country/TerritoryJapan
CityVirtual, Online
Period21/5/821/5/13

Keywords

  • Activity recognition
  • Machine learning
  • Sensor simulation

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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