Binary and Hybrid Work-Condition Maps for Interactive Exploration of Ergonomic Human Arm Postures

Luka Peternel*, Daniel Tofte Schøn, Cheng Fang

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Ergonomics of human workers is one of the key elements in design and evaluation of production processes. Human ergonomics have a major impact on productivity as well as chronic health risks incurred by inappropriate working postures and conditions. In this paper we propose a novel method for estimating and communicating the ergonomic work condition called Binary Work-Condition Map, which provides a visualized feedback about work conditions of different configurations of an arm. The map is of binary nature and is derived by imposing the desired thresholds on considered ergonomic and safety related criteria. Therefore, the suggested arm postures in the map guarantee that all considered criteria are satisfied. This eliminates the ambiguity compared to state-of-the-art maps that uses continuous scales derived from weighted sum of multiple ergonomics criteria. In addition, to combine the advantages of both the binary map and the continuous map, we additionally propose a Hybrid Work-Condition Map that rules out unsuitable workspace with the binary map approach and renders the suitable workspace with the continuous map approach. The proposed approach was tested in simulation for various tasks and conditions. In addition, we conducted subjective evaluation experiments to compare the proposed methods with the state-of-the art method regarding the usability. The results indicated that the binary map is simpler to use, while the hybrid map is a good tradeoff between the binary and the continuous map. In selecting the map, strong points of each map should be considered with respect to the requirements of a specific application and task.

TidsskriftFrontiers in Neurorobotics
Antal sider13
StatusUdgivet - jan. 2021

Bibliografisk note

Funding Information:
This work was partially supported by project “MADE Digital -driving growth and productivity in manufacturing through digitalization” funded by Innovation Fund Denmark, and it was also supported by the SDU I4.0 initiative.

Publisher Copyright:
© Copyright © 2021 Peternel, Schøn and Fang.

Copyright 2021 Elsevier B.V., All rights reserved.


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