A Multi-Modal Feedback Communication Interface for Human Working Posture Adjustments

Conference Paper (2023)
Author(s)

Kushal Thirani (Student TU Delft)

D. Abbink (TU Delft - Human-Robot Interaction)

Luka Peternel (TU Delft - Human-Robot Interaction)

Research Group
Human-Robot Interaction
Copyright
© 2023 Kushal Thirani, D.A. Abbink, L. Peternel
DOI related publication
https://doi.org/10.1007/978-3-031-22731-8_2
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Kushal Thirani, D.A. Abbink, L. Peternel
Research Group
Human-Robot Interaction
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
14-29
ISBN (print)
978-3-031-22730-1
ISBN (electronic)
978-3-031-22731-8
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

This paper studies non-physical feedback mechanisms to guide human workers toward ergonomic body postures. Specifically, the focus is to solve the tasks that involve no direct physical interaction between the human and the robotic system, therefore tactile guidance by the robot body is not feasible. We propose a multi-modal ergonomic posture guidance system that comprises visual feedback and speech-based audio feedback. We hypothesise that the proposed multi-modal system leads to better performance compared to uni-modal feedback systems when trying to guide users from one pose to another. To test the hypothesis we conducted an experiment that compared conditions with only audio feedback, only visual feedback and multi-modal feedback. In addition, we examined speech-based audio guidance in joint space and in endpoint space. The results showed that the speech-based feedback in joint space came out as the preferred audio feedback due to its ability to allow users to carry out efficient and coordinated inter-joint movements, especially in cases of high redundancy. Furthermore, the proposed multi-modal feedback system was superior compared to the other feedback modalities both in terms of objective measures and subjective measures.

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