A Digital Twin-Based Multi-modal UI Adaptation Framework for Assistance Systems in Industry 4.0

被引:8
作者
Josifovska, Klementina [1 ]
Yigitbas, Enes [1 ]
Engels, Gregor [1 ]
机构
[1] Paderborn Univ, Paderborn, Germany
来源
HUMAN-COMPUTER INTERACTION. DESIGN PRACTICE IN CONTEMPORARY SOCIETIES, HCI 2019, PT III | 2019年 / 11568卷
关键词
Adaptive user interface; Digital Twin; Industry; 4.0;
D O I
10.1007/978-3-030-22636-7_30
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
As a consequence of digital transformation many aspects related to the industrial manufacturing processes are facing changes. In terms of Human-Machine Interaction, the User Interface (UI) plays the most important role as a mediator between the human and certain assistance systems. In traditional industrial environments, the UIs are usually designed to handle a unimodal input command (via touch screen, keyboard or mouse) and to present a feedback in a visual way. However, due to the nature of the tasks there is a need for the human workers to easily shift tasks and acquire new skills. For this reason, in the UI adaptation process the personal abilities and preferences of the human workers should be taken into consideration. In this paper, we present a novel reference model for multi-modal adaptive UIs for assistance systems in manufacturing processes. Our approach provides a solution framework for adaptation of assistance systems in manufacturing processes not only based on the environmental conditions, but also based on the personal characteristics and abilities of the human workers, obtained by a personalized Digital Twin.
引用
收藏
页码:398 / 409
页数:12
相关论文
共 50 条
  • [41] Digital Twin-Based Operation Simulation System and Application Framework for Electromechanical Products
    Lu, Yang
    Qiu, Xiaoli
    Xing, Yan
    2021 INTERNATIONAL CONFERENCE ON COMPUTER, CONTROL AND ROBOTICS (ICCCR 2021), 2021, : 146 - 150
  • [42] A Digital Twin-Based Platform for Medical Cyber-Physical Systems
    Rahim, Messaoud
    Lalouani, Wassila
    Toubal, Elbahi
    Emokpae, Lloyd
    IEEE ACCESS, 2024, 12 : 174591 - 174607
  • [43] A Digital Twin-based scheduling framework including Equipment Health Index and Genetic Algorithms
    Negri, E.
    Ardakani, H. Davari
    Cattaneo, L.
    Singh, J.
    Macchi, M.
    Lee, J.
    IFAC PAPERSONLINE, 2019, 52 (10): : 43 - 48
  • [44] Digital twin-based cyber physical production system architectural framework for personalized production
    Kyu Tae Park
    Jehun Lee
    Hyun-Jung Kim
    Sang Do Noh
    The International Journal of Advanced Manufacturing Technology, 2020, 106 : 1787 - 1810
  • [45] A blockchain-based interactive approach between digital twin-based manufacturing systems
    Liu, Shimin
    Lu, Yuqian
    Li, Jie
    Shen, Xingwang
    Sun, Xuemin
    Bao, Jinsong
    COMPUTERS & INDUSTRIAL ENGINEERING, 2023, 175
  • [46] Digital twin-based reinforcement learning framework: application to autonomous mobile robot dispatching
    Jaoua, Amel
    Masmoudi, Samar
    Negri, Elisa
    INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2024, 37 (10-11) : 1335 - 1358
  • [47] Digital Twin-Based Transfer Learning for Collaborative Robot Systems: A Proof of Concept
    Roongpraiwan, Supat
    Li, Zongdian
    Pourghasemian, Mohsen
    Gacanin, Haris
    Sakaguchi, Kei
    38TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, ICOIN 2024, 2024, : 593 - 598
  • [48] A digital twin-based framework for simulation and monitoring analysis of floating wind turbine structures
    Liu, Yi
    Zhang, Jian-Min
    Min, Yan-Tao
    Yu, Yantao
    Lin, Chao
    Hu, Zhen-Zhong
    OCEAN ENGINEERING, 2023, 283
  • [49] Digital twin-based cyber physical production system architectural framework for personalized production
    Park, Kyu Tae
    Lee, Jehun
    Kim, Hyun-Jung
    Noh, Sang
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2020, 106 (5-6) : 1787 - 1810
  • [50] A Digital Twin-Based Production-Maintenance Joint Scheduling Framework with Reinforcement Learning
    Hao, Qinglong
    Lv, Yaqiong
    2023 8TH INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTICS ENGINEERING, ICCRE, 2023, : 51 - 56