Context-aware AR adaptive information push for product assembly: Aligning information load with human cognitive abilities

Augmented Reality (AR) innovates product assembly by overlaying intuitive visual instructions onto the physical workspace, improving operational efficiency. However, existing AR-guided assembly methods has the following drawbacks. Most studies lack consideration of the dynamic adaptation of operators’ cognitive abilities. Excessive or insufficient information push may cause cognitive overload or information underutilization, which has instead a negative impact on assembly work. In addition, due to lack of automatic perceiving assembly state, frequent human-interface interactions interferences the workflow continuity, which is particularly disadvantageous for manual assembly work. To address the problems, we propose an intelligent AR-guided adaptive information push (AR-AIP) method based on context-awareness, which personalizes information push according to individual cognitive abilities. Firstly, the method comprehensively assesses human cognitive abilities by separately considering factors such as fatigue state and skill level. Secondly, it quantifies the information load associated with various visual representations of AR content, aiming to identify the most suitable combination that aligns with each user's cognitive abilities. Thirdly, the perception of the assembly process and its state is fundamental for achieving adaptive content push. This study, therefore, employs context awareness by integrating the recognition of assembly actions and the detection of parts, facilitating the automatic inference of the assembly process and its state. The AR-AIP aims at three “rights”, i.e., push the right information in the right AR representation forms to the operator at the right time, proactively and automatically, with minimal manual interactions. This approach is designed to alleviate the cognitive burden on operators, thereby enhancing assembly quality and efficiency. A comparative study on electronic equipment assembly shows that compared to traditional AR methods, AR-AIP significantly improves task completion time, reduces errors, lowers cognitive load, enhances user experience while minimizing skill and fatigue variations. The research findings offer insights for designing AR visual content to assist assembly tasks, providing a new approach for proactive perception and understanding of real-time assembly states.