Intuitive interaction flow: A Dual-Loop Human-Machine Collaboration Task Allocation Model and an experimental study
October 10, 2024 Β· Declared Dead Β· π Design and Artificial Intelligence
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Authors
Jiang Xu, Qiyang Miao, Ziyuan Huang, Yilin Lu, Lingyun Sun, Tianyang Yu, Jingru Pei, Qichao Zhao
arXiv ID
2410.07804
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
Design and Artificial Intelligence
Last Checked
5 months ago
Abstract
This study investigates the issue of task allocation in Human-Machine Collaboration (HMC) within the context of Industry 4.0. By integrating philosophical insights and cognitive science, it clearly defines two typical modes of human behavior in human-machine interaction(HMI): skill-based intuitive behavior and knowledge-based intellectual behavior. Building on this, the concept of 'intuitive interaction flow' is innovatively introduced by combining human intuition with machine humanoid intelligence, leading to the construction of a dual-loop HMC task allocation model. Through comparative experiments measuring electroencephalogram (EEG) and electromyogram (EMG) activities, distinct physiological patterns associated with these behavior modes are identified, providing a preliminary foundation for future adaptive HMC frameworks. This work offers a pathway for developing intelligent HMC systems that effectively integrate human intuition and machine intelligence in Industry 4.0.
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