Intuitive interaction flow: A Dual-Loop Human-Machine Collaboration Task Allocation Model and an experimental study

TL;DR

This paper introduces a dual-loop task allocation model for human-machine collaboration in Industry 4.0, integrating cognitive science and physiological data to distinguish human behavior modes and enhance adaptive interaction.

Contribution

It proposes the innovative concept of 'intuitive interaction flow' and constructs a dual-loop HMC task allocation model based on physiological patterns.

Findings

Distinct EEG and EMG patterns linked to behavior modes

Experimental validation of the dual-loop model

Foundation for adaptive HMC frameworks

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.