Intuitive interaction flow: A Dual-Loop Human-Machine Collaboration Task Allocation Model and an experimental study
<|reference_start|>Intuitive interaction flow: A Dual-Loop Human-Machine Collaboration Task Allocation Model and an experimental study: 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.<|reference_end|>
@article{xu2024intuitive,
    title={Intuitive interaction flow: A Dual-Loop Human-Machine Collaboration Task
  Allocation Model and an experimental study},
    author={Jiang Xu and Qiyang Miao and Ziyuan Huang and Yilin Lu and Lingyun Sun
  and Tianyang Yu and Jingru Pei and Qichao Zhao},
    journal={arXiv preprint arXiv:2410.07804},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07804},
    primaryClass={cs.HC}
}