  An adaptive guidance system that supports equipment operators requires a
comprehensive model, which involves a variety of user behaviors that considers
different skill and knowledge levels, as well as rapid-changing task
situations. In the present paper, we introduced a novel method for modeling
operational tasks, aiming to integrate visual operation records provided by
users with diverse experience levels and personal characteristics. For this
purpose, we investigated the relationships between user behavior patterns that
could be visually observed and their skill levels under machine operation
conditions. We considered 144 samples of two sewing tasks performed by 12
operators using a head-mounted RGB-D camera and a static gaze tracker.
Behavioral features, such as the operator's gaze and head movements, hand
interactions, and hotspots, were observed with significant behavioral trends
resulting from continuous user skill improvement. We used a two-step method to
model the diversity of user behavior: prototype selection and experience
integration based on skill ranking. The experimental results showed that
several features could serve as appropriate indices for user skill evaluation,
as well as providing valuable clues for revealing personal behavioral
characteristics. The integration of user records with different skills and
operational habits allowed developing a rich, inclusive task model that could
be used flexibly to adapt to diverse user-specific needs.
