<p>Robotic-assisted surgery (RAS) is one of the most
significant advancements in surgical techniques in the past three decades. It
provides benefits of reduced infection risks and shortened recovery time over
open surgery as well as improved dexterity, stereoscopic vision, and ergonomic
console over laparoscopic surgery. The prevalence of RAS systems has increased
over years and is expected to grow even larger. However, the major concerns of
RAS are the technical difficulty and the system complexity, which can result in
long learning time and impose extra cognitive workload and stress on the operating
room. Human Factor and Ergonomics (HFE) perspective is critical to patient
safety and relevant researches have long provided methods to improve surgical
outcomes. Yet, limited studies especially using objective measurements, have been
done in the RAS environment. </p>
<p> </p>
<p>With advances in wearable sensing technology and data
analytics, the applications of physiological measures in HFE have been ever
increasing. Physiological measures are objective and real-time, free of some main
limitations in subjective measures. Eye tracker as a minimally-intrusive and
continuous measuring device can provide both physiological and behavioral
metrics. These metrics have been found sensitive to changes in workload in various
domains. Meanwhile, electroencephalography (EEG) signals capture electrical
activity in the cerebral cortex and can reflect cognitive processes that are
difficult to assess with other objective measures. Both techniques have the
potential to help address some of the challenges in RAS.</p>
<p> </p>
<p>In this study, eight RAS trainees participated in a 3-month
long experiment. In total, they completed 26 robotic skills simulation
sessions. In each session, participants performed up to 12 simulated RAS
exercises with varying levels of difficulty. For Research Question I,
correlation and mixed effect analyses were conducted to explore the
relationships between eye tracking metrics and workload. Machine learning
classifiers were used to determine the sensitivity of differentiating low and
high workload with eye tracking metrics. For Research Question II, two eye
tracking metrics and one EEG metric were used to explain participants’ performance
changes between consecutive sessions. Correlation and ANOVA analyses were
conducted to examine whether variations in performance had significant relationships
with variations in objective metrics. Classification models were built to
examine the capability of objective metrics in predicting improvement during
RAS training. </p>
<p> </p>
<p>In Research Question I, pupil diameter and gaze entropy
distinguished between different task difficulty levels, and both metrics
increased as the level of difficulty increased. Yet only gaze entropy was
correlated with subjective workload measurement. The classification model
achieved an average accuracy of 89.3% in predicting workload levels. In Research
Question II, variations in gaze entropy and engagement index were negatively
correlated with variations in task performance. Both metrics tended to decrease
when performance increased. The classification model achieved an average
accuracy of 68.5% in predicting improvements.</p>
<p> </p>
<p>Eye tracking metrics can measure both task workload and perceived
workload during simulated RAS training. It can potentially be used for real-time
monitoring of workload in RAS procedure to identify task contributors to high
workload and provide insights for training. When combined with EEG, the objective
metrics can explain the performance changes during RAS training, and help
estimate room for improvements.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9108653 |
| Date | 16 August 2019 |
| Creators | Chuhao Wu (7043360) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/EYE_TRACKING_AND_ELECTROENCEPHALOGRAM_EEG_MEASURES_FOR_WORKLOAD_AND_PERFORMANCE_IN_ROBOTIC_SURGERY_TRAINING/9108653 |
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