Hotspots Integrating of Expert and Beginner Experiences of Machine Operations through Egocentric Vision

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

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
243 Downloads (Pure)


Beginners can often provide useful information regarding machine operations, e.g., how difficult a specific operation is or how beginners deal with difficulties. However, their experiences oftentimes widely vary, and it is difficult to summarize these diverse experiences in an extensive way. To try and solve this problem, this study focused on developing a framework for integrating beginners' and experts' experiences into a unified operation model. A baseline model was first obtained based on hand{machine interactions automatically extracted from experts' egocentric vision records. Then, the beginners' behaviours were integrated by dynamically aligning them to the baseline model. Through this process, an integrated model based on the experiences of a wide range of users was achieved. We applied our method to the operation experiences of two tabletop devices, an IH heater and a sewing machine. The results show good potentials in modelling the common and different behaviours among experts and beginners.
Original languageEnglish
Title of host publicationInternational Conference on Machine Vision Applications
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)978-4-901122-18-4
Publication statusE-pub ahead of print - 11 Jul 2019
EventInternational Conference on Machine Vision Applications - Tokyo, Japan
Duration: 27 May 201931 May 2019
Conference number: 16


ConferenceInternational Conference on Machine Vision Applications
Abbreviated titleMVA
Internet address


  • computer vision
  • human computer interaction
  • sewing machines
  • mechanical engineering computing


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