Development of a cooperative work method based on autonomous learning of implicit instructions

Lena Guinot, Yukiko Iwasaki, Shota Takahashi, Hiroyasu Iwata

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

1 Citation (Scopus)


Cooperative work with wearable robotics designed as an “extended body” for the wearer has the potential to improve individual productivity regardless of the context. The final purpose of this research it to design a new communication method between the wearer and a wearable robot arm as they perform daily chores simultaneously. Among previous studies on wearable robot arms, very little quantify the magnitude of the impact of robot operation on attention distribution and psychological burden for the user. The present paper presents an approach based on the idea that the robot arm could understand human intentions by reading implicit instruction cues nested in the natural motion flow of the operator performing a task. The present paper describes an Inertial Measurement Unit (IMU) sensor data - deep learning approach that enables the robot arm to learn these cues. The validity of the method was evaluated on three indexes: implicit instruction estimation accuracy, secondary task completion quality, and cognitive burden for the wearer. Results showed considerable improvement on all these proposed axes compared to other explicit operation methods (such as voice instructions), along with better results than similar implicit instruction-based researches.

Original languageEnglish
Title of host publicationProceedings of the 11th Augmented Human International Conference, AH 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450377287
Publication statusPublished - 2020 May 27
Event11th Augmented Human International Conference, AH 2020 - Winnipeg, Canada
Duration: 2020 May 272020 May 29

Publication series

NameACM International Conference Proceeding Series


Conference11th Augmented Human International Conference, AH 2020


  • Deep learning
  • IMU sensor
  • Interface design
  • Robot collaboration
  • Robot control

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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