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The efficient execution of surgical operations plays a crucial role in
optimizing patient outcomes, evidencing the need for effective training
methods to improve surgical skills. The gaining traction of medical
training simulators for automated skill assessment necessitates
instrumented sensors and relevant metrics for targeted feedback on all
aspects of a surgical procedure. Traditional metrics that capture a
single instance of force, such as peak or range, lack the
characterization of the entire force profile and lose subtleties that
may limit accurate evaluation of the skilled application of force, a
valuable aspect of assessment in surgery. This study introduces novel
force metrics inspired by motion smoothness-based measures, analyzed on
an extensive dataset of 97 subjects suturing on an open vascular
suturing simulator. We validated the effectiveness of these metrics by
comparing the metrics’ ability to distinguish between subject skill
levels. Our findings highlight the value of these advanced force metrics
as robust indicators of suturing performance, demonstrating their
valuable potential for more accurate and objective skill assessment in
surgical training. Clinical Relevance-The force metrics presented in
this study analyze the intricacies of the widespread category of
assessment in surgery, “respect for tissue”, greatly benefiting
surgical education with an improved evaluation of this aspect of
suturing skill.