Hotspot modeling of hand-machine interaction experiences from a head-mounted RGB-D camera

Longfei Chen, Yuichi Nakamura, Kazuaki Kondo, Walterio Mayol-Cuevas

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
218 Downloads (Pure)

Abstract

This paper presents an approach to analyze and model tasks of machines being operated. The executions of the tasks were captured through egocentric vision. Each task was decomposed into a sequence of physical hand-machine interactions, which are described with touch-based hotspots and interaction patterns. Modeling the tasks was achieved by integrating the experiences of multiple experts and using a hidden Markov model (HMM). Here, we present the results of more than 70 recorded egocentric experiences of the operation of a sewing machine. Our methods show good potential for the detection of hand-machine interactions and modeling of machine operation tasks.

Original languageEnglish
Pages (from-to)319-330
Number of pages12
JournalIEICE Transactions on Information and Systems
Issue number2
DOIs
Publication statusPublished - 1 Feb 2019

Keywords

  • Egocentric vision
  • Hotspots
  • Machine operation experiences
  • RGB-D
  • Task modeling

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