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Systematic Approach to Develop a Flexible Adaptive Human-Machine Interface in Socio-Technological Systems

  • Julia N. Czerniak
  • Valeria Villani
  • Lorenzo Sabattini
  • Frieder Loch
  • Birgit Vogel-Heuser
  • Cesare Fantuzzi
  • Christopher Brandl
  • Alexander Mertens
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 825)

Abstract

Modern automatic machines in production have been becoming more and more complex within the recent years. Thus, human-machine interfaces (HMI) reflect multiple different functions. An approach to improve human-machine interaction can be realised by adjusting the HMI to the operators’ requirements and complementing their individual skills and capabilities, supporting them in self-reliant machine operation. Based on ergonomic concepts of information processing, we present a systematic approach for developing an adaptive HMI after the MATE concept (Measure, Adapt & Teach). In a first step, we develop a taxonomy of human capabilities that have an impact on individual performance during informational work tasks with machine HMI. We further evaluate three representative use cases by pairwise comparison regarding the classified attributes. Results show that cognitive information processes, such as different forms of attention and factual knowledge (crystalline intelligence) are most relevant on average. Moreover, perceptive capabilities that are restricted by task environment, e.g. several auditory attributes; as well as problem solving demand further support, according to the experts’ estimation.

Keywords

Adaptive HMI Human abilities Human-machine interaction Taxonomy MATE concept Performance 

Notes

Acknowledgements

The research is carried out within the “Smart and Adaptive Interfaces for INCLUSIVE Work Environment” project, funded by the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement N723373. The authors would like to express their gratitude for the support given.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Julia N. Czerniak
    • 1
  • Valeria Villani
    • 2
  • Lorenzo Sabattini
    • 2
  • Frieder Loch
    • 3
  • Birgit Vogel-Heuser
    • 3
  • Cesare Fantuzzi
    • 2
  • Christopher Brandl
    • 1
  • Alexander Mertens
    • 1
  1. 1.Institute of Industrial Engineering and ErgonomicsRWTH Aachen UniversityAachenGermany
  2. 2.Department of Sciences and Methods for EngineeringUniversity of Modena and Reggio EmiliaReggio EmiliaItaly
  3. 3.Institute of Automation and Information SystemsTechnical University of MunichGarching, MunichGermany

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