TY - GEN
T1 - Sparse hidden Markov models for surgical gesture classification and skill evaluation
AU - Tao, Lingling
AU - Elhamifar, Ehsan
AU - Khudanpur, Sanjeev
AU - Hager, Gregory D.
AU - Vidal, René
PY - 2012
Y1 - 2012
N2 - We consider the problem of classifying surgical gestures and skill level in robotic surgical tasks. Prior work in this area models gestures as states of a hidden Markov model (HMM) whose observations are discrete, Gaussian or factor analyzed. While successful, these approaches are limited in expressive power due to the use of discrete or Gaussian observations. In this paper, we propose a new model called sparse HMMs whose observations are sparse linear combinations of elements from a dictionary of basic surgical motions. Given motion data from many surgeons with different skill levels, we propose an algorithm for learning a dictionary for each gesture together with an HMM grammar describing the transitions among different gestures. We then use these dictionaries and the grammar to represent and classify new motion data. Experiments on a database of surgical motions acquired with the da Vinci system show that our method performs on par with or better than state-of-the-art methods.This suggests that learning a grammar based on sparse motion dictionaries is important in gesture and skill classification.
AB - We consider the problem of classifying surgical gestures and skill level in robotic surgical tasks. Prior work in this area models gestures as states of a hidden Markov model (HMM) whose observations are discrete, Gaussian or factor analyzed. While successful, these approaches are limited in expressive power due to the use of discrete or Gaussian observations. In this paper, we propose a new model called sparse HMMs whose observations are sparse linear combinations of elements from a dictionary of basic surgical motions. Given motion data from many surgeons with different skill levels, we propose an algorithm for learning a dictionary for each gesture together with an HMM grammar describing the transitions among different gestures. We then use these dictionaries and the grammar to represent and classify new motion data. Experiments on a database of surgical motions acquired with the da Vinci system show that our method performs on par with or better than state-of-the-art methods.This suggests that learning a grammar based on sparse motion dictionaries is important in gesture and skill classification.
KW - Surgical skill evaluation
KW - hidden Markov models
KW - sparse dictionary learning
KW - surgical gesture classification
KW - time series classification
UR - http://www.scopus.com/inward/record.url?scp=84864297275&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864297275&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-30618-1_17
DO - 10.1007/978-3-642-30618-1_17
M3 - Conference contribution
AN - SCOPUS:84864297275
SN - 9783642306174
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 167
EP - 177
BT - Information Processing in Computer-Assisted Interventions - Third International Conference, IPCAI 2012, Proceedings
T2 - 3rd International Conference on Information Processing in Computer-Assisted Interventions, IPCAI 2012
Y2 - 27 June 2012 through 27 June 2012
ER -