@proceedings {1990,
	title = {Towards Automated Performance Assessment using Velocity-based Motion Quality Metrics},
	year = {2020},
	month = {11/2020},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/ISMR_2020_Murali_et_al_FinalVersion_0.pdf},
	author = {Barathwaj Murali and Viony Belvroy and Shivam Pandey and Michael D. Byrne and Jean Bismuth and Marcia K. O{\textquoteright}Malley}
}
@proceedings {1890,
	title = {Toward training surgeons with motion-based feedback: Initial validation of smoothness as a measure of motor learning},
	volume = {61},
	number = {1},
	year = {2017},
	pages = {1531-1535},
	abstract = {<p>Surgery 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{\textemdash}spectral arc length{\textemdash}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.</p>},
	doi = {10.1177/1541931213601747},
	url = {https://doi.org/10.1177/1541931213601747},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/pandey2017hfes.pdf},
	author = {Shivam Pandey and Michael D. Byrne and William H. Jantscher and Marcia K. O{\textquoteright}Malley and Priyanshu Agarwal}
}
