An adaptive guidance system that supports equipment operators requires a comprehensive model of task and user behavior that considers different skill and knowledge levels as well as diverse situations. In this study , we investigated the relationships between user behaviors and skill levels under operational conditions. We captured sixty samples of two sewing tasks performed by five operators using a head-mounted RGB-d camera and a static gaze tracker. We examined the operators' gazeand head movements, and hand interactions to essential regions (hotspotson machine surface) to determine behavioral differences among continuous skill improving experiences. We modeled the variety of user behaviors to an extensive task model with a two-step automatic approach, baseline model selection and experience integration. The experimental results indicate that some features, such as task execution time and user head movements, are good indexes for skill level and provide valuable information that can be applied to obtain an effective task model. Operators with varying knowledge and operating habits demonstrate different operational features, which can contribute to the design of user-specific guidance .
An adaptive guidance system that supports equipment operators requires a comprehensive model of task and user behavior that considers different skill and knowledge levels as well as diverse situations. In the present paper, we introduced a novel method for machine operation modeling aimed to integrate visual operation records provided by users with different skills, knowledge levels, and interpersonal behavior patterns. 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 sixty samples of two sewing tasks performed by five operators using a head-mounted RGB-D camera and a static gaze tracker. We examined behavioral features, such as the operator gaze, head movements, and hand interactions with hotspots, and observed significant behavioral changes as a result of continuous skill improvement. We automatically modeled the variety of behaviors of operation tasks with a two-step approach, prototype selection and experiences integration. The experimental results indicated that features, such as duration of task execution and user head movements, could serve as appropriate indices for skill level evaluation, and provide useful information for integrating various records corresponding to different skill levels and behavioral characteristics. Integrating operation records with operating habits allowed developing a rich inclusive task model that could be used to flexibly adapt to various user-specific behavior patterns .
R
this study
the present paper, we introduced a novel method for machine operation modeling aimed to integrate visual operation records provided by users with different skills, knowledge levels, and interpersonal behavior patterns. For this purpose
R
behaviors and
behavior patterns that could be visually observed and their
R
operational
machine operation
R
captured
considered
R
RGB-d
RGB-D
R
the operators' gazeand
behavioral features, such as the operator gaze,
R
to essential regions (hotspotson machine surface) to determine behavioral differences among continuous skill improving experiences. We
with hotspots, and observed significant behavioral changes as a result of continuous skill improvement. We automatically
R
user behaviors to an extensive task model
behaviors of operation tasks
R
automatic approach, baseline model selection and experience
approach, prototype selection and experiences
R
indicate that some
indicated that
R
task execution time
duration of task execution
R
are good indexes
could serve as appropriate indices
R
and provide valuable information that can be applied to obtain an effective task model. Operators with varying knowledge and operating habits demonstrate different operational features, which can contribute to the design of
evaluation, and provide useful information for integrating various records corresponding to different skill levels and behavioral characteristics. Integrating operation records with operating habits allowed developing a rich inclusive task model that could be used to flexibly adapt to various
R
guidance
behavior patterns