Abstract
Visual inspection is used in many areas due to the potential high costs of inspection error such as injury, fatality, loss of expensive equipment, scrapped items, rework, or failure to procure repeat business. This study presents an application of hidden Markov models (HMM) to fixations’ sequences analysis during visual inspection of front panels in a home appliance facility. The eye tracking data are gathered when quality control operators perform their tasks. The results support the difference between expert and novice operator. Moreover, the article demonstrates four HMMs with two and three hidden states both for novice and experienced operators and provides analysis and discussion of the outcomes.
Highlights
Inspection is defined as a deliberate, in-depth, exacting process that requires more than mere looking or scanning (See 2012). Drury and Prabhu (1992) point out precision, depth, and validity are critical elements of the definition
This study presents an application of hidden Markov models (HMM) to fixations’ sequences analysis during visual inspection of front panels in a home appliance facility
Since identified areas of interests (AOIs) are differentiated by their importance to the manufacturer, the presented hidden states from models may correspond to some combination of defect type and importance components of the quality control process
Summary
Inspection is defined as a deliberate, in-depth, exacting process that requires more than mere looking or scanning (See 2012). Drury and Prabhu (1992) point out precision, depth, and validity are critical elements of the definition. HMM tools allow for finding stochastic relations between observations and hidden states and, are very useful in broadening the basic knowledge about attentional visual processes. Greene et al (2012) use a linear discriminant classifier to verify if the complex mental states to be inferred from eye tracking data as it was suggested by Yarbus (1967) whereas Coutrot et al (2018) provide a method for scan path modeling and classification using variational HMMs and discriminant analysis. HMMs are only used for profiling subjects and the authors did not include any substantial analysis of participants’ visual attentional behavior Another possible application of employing HMMs in the industrial environment may be related to automatic face recognition. The outcomes of the HMM simulation results are discussed and the paper is concluded with directions for future studies
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