抄録
In assembly work, identifying individual skill levels of workers and situations in which they need assistance are critical to providing assistance by a system without interfering with other workers. We hypothesized that the determination of type and level of confusion that occurs during assembly work can facilitate the identification of worker states. In this paper, we present a method for confusion detection and classification. Positional information from the hand and gaze was used to detect the presence of confusion if any, confusion type (i.e., searching for assembly parts and mounting the parts in a specific position), and its strength (i.e., weak and strong confusion). We used two types of classification features: gaze-transition and histogram-based. Moreover, to improve the confusion-classification performance, we classified confusion in a hierarchical manner. The results shows that F1-scores of 0.529 and 0.511 were obtained in the five-class classification with and without hierarchical classifier formation, respectively. We also integrated the classification pipeline into a working-system prototype using reject-option processing. A user study was conducted to validate classification performance.
https://ror.org/00qg0kr10 
