Browsing by Author "Jantscher, William H."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Toward training surgeons with motion-based feedback: Initial validation of smoothness as a measure of motor learning(Sage, 2017) Pandey, Shivam; Byrne, Michael D.; Jantscher, William H.; O’Malley, Marcia K.; Agarwal, PriyanshuSurgery is a challenging domain for motor skill acquisition. A critical contributing factor in this difficulty is that feedback is often delayed from performance and qualitative in nature. Collection of highdensity motion information may offer a solution. Metrics derived from this motion capture, in particular indices of movement smoothness, have been shown to correlate with task outcomes in multiple domains, including endovascular surgery. The open question is whether providing feedback based on these metrics can be used to accelerate learning. In pursuit of that goal, we examined the relationship between a motion metric that is computationally simple to compute—spectral arc length—and performance on a simple but challenging motor task, mirror tracing. We were able to replicate previous results showing that movement smoothness measures are linked to overall performance, and now have performance thresholds to use in subsequent work on using these metrics for training.Item Using Real-Time Smoothness Metrics to Deliver Haptic Performance Cues for a Dexterous Task(2018-04-17) Jantscher, William H.; O'Malley, Marcia KSurgeons representing a number of domains, including endovascular surgery, have expressed a desire for objective performance measures and feedback mechanisms as a means to improve training and assessment of skill. Metrics that quantify smoothness of tool movements have recently been shown to exhibit strong correlations with endovascular surgical skill. This thesis explores the applicability and implementation of two of these smoothness metrics as a method for providing real-time performance feedback via a haptic interface. The algorithms for spectral arc length and submovement count are modi ed for near real-time computation. They are then mapped to vibrotactile cues to serve as a feedback when subjects complete a dexterous, mirror tracing task. This task is developed and veri ed as a suitable proxy task for endovascular surgery and serves as a platform to test performance assessment and feedback based on movement smoothness analysis. Submovement count is shown to have limited viability as it is constrained by its computationally expensive nature. However, spectral arc length based feedback is shown to have a substantial positive e ect on performance in terms of both accuracy and completion time.