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An incremental learning approach to detect muscular fatigue in human-robot collaboration

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posted on 2023-03-17, 16:35 authored by Achim Buerkle, Ali Al-Yacoub, William Eaton, Melanie Zimmer, Thomas Bamber, Pedro FerreiraPedro Ferreira, Ella-Mae HubbardElla-Mae Hubbard, Niels Lohse

Human–robot collaboration aims to join the distinctive strengths of humans and robots to compensate for the weaknesses associated with each party and, thus, to enable synergetic effects. Robots are characteristically considered fatigue-proof. Hence, they are utilized to assist human operators during heavy pushing and pulling activities. To detect physical fatigue or high payloads held by a human operator, wearable sensors, such as electromyographys (EMGs), are deployed. The EMG data are typically processed via machine learning, which includes training models offline before an application in an online system. However, these approaches often demonstrate varying performances between offline and online applications due to subject-specific characteristics within the data. An opportunity to tackle this challenge can be found in incremental learning, as these models purely learn online and constantly fine-tune the model's structure. In this article, a Mondrian Forest is applied to predict payloads and physical fatigue of human operators during an assistance scenario with a collaborative robot. An experiment was conducted with a total of 12 participants, where the payload was increased until participants initiated an assistance request from a Universal Robots model 10 cobot. This allowed for testing whether the Mondrian Forest can accurately predict the payload and fatigue levels from the acquired EMG signals. Overall, the approach demonstrates a promising potential toward higher awareness when an operator might require assistance from a robot and ultimately toward a more effective human–robot collaboration.

Funding

Digital Toolkit for optimisation of operators and technology in manufacturing partnerships (DigiTOP)

Engineering and Physical Sciences Research Council

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History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Human-Machine Systems

Volume

53

Issue

3

Pages

520 - 528

Publisher

Institute of Electrical and Electronics Engineers

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2023-03-15

Publication date

2023-03-30

Copyright date

2023

ISSN

2168-2291

eISSN

2168-2305

Language

  • en

Depositor

Dr Ella-Mae Hubbard. Deposit date: 17 March 2023

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