title: Automatic recognition of surgical motions using statistical modeling for capturing variability.
abstract: The ability to accurately recognize elementary surgical gestures is a stepping stone to automated surgical assessment and surgical training. However, as the pool of subjects increases, variation in surgical techniques and unanticipated motion increases the challenge of creating robust statistical models of gestures. This paper examines the applicability of advanced modeling techniques from automated speech recognition to the problem of increasing variability in surgical motions. In particular, we demonstrate the effectiveness of automatically bootstrapped user-adaptive models on diverse data acquired from the da Vinci surgical robot.
authors.affiliation: Johns Hopkins University, Baltimore, MD 21211, USA. creiley@cs.jhu
authors.forename: Carol E
authors.initials: CE
authors.lastname: Reiley
authors.forename: Henry C
authors.initials: HC
authors.lastname: Lin
authors.forename: Balakrishnan
authors.initials: B
authors.lastname: Varadarajan
authors.forename: Balazs
authors.initials: B
authors.lastname: Vagvolgyi
authors.forename: Ssanjeev
authors.initials: S
authors.lastname: Khudanpur
authors.forename: David D
authors.initials: DD
authors.lastname: Yuh
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Hager
mesh_terms: D003198:Computer Simulation; D013502:General Surgery / Q000379:methods*; D005868:Gestures; D006801:Humans; D015233:Models, Statistical; D012371:Robotics; D049250:Speech Recognition Software; D014481:United States; D014584:User-Computer Interface