Intuitive interaction flow: A Dual-Loop Human-Machine Collaboration Task Allocation Model and an experimental study
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929547355619328 |
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| author | Xu, Jiang Miao, Qiyang Huang, Ziyuan Lu, Yilin Sun, Lingyun Yu, Tianyang Pei, Jingru Zhao, Qichao |
| author_facet | Xu, Jiang Miao, Qiyang Huang, Ziyuan Lu, Yilin Sun, Lingyun Yu, Tianyang Pei, Jingru Zhao, Qichao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07804 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intuitive interaction flow: A Dual-Loop Human-Machine Collaboration Task Allocation Model and an experimental study Xu, Jiang Miao, Qiyang Huang, Ziyuan Lu, Yilin Sun, Lingyun Yu, Tianyang Pei, Jingru Zhao, Qichao Human-Computer Interaction 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. |
| title | Intuitive interaction flow: A Dual-Loop Human-Machine Collaboration Task Allocation Model and an experimental study |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.07804 |