TY - GEN
T1 - Measuring movement expertise in surgical tasks
AU - Kahol, Kanav
AU - Krishnan, Narayanan C.
AU - Balasubramanian, Vineeth N.
AU - Panchanathan, Sethuraman
AU - Smith, Marshall
AU - Ferrara, John
PY - 2006
Y1 - 2006
N2 - Surgical movement is composed of discrete gestures that are combined to perform complex surgical procedures. A promising approach to objective surgical skill evaluation systems is kinematics and kinetic analysis of hand movement that yields a gesture level analysis of proficiency of a performed movement. In this paper, we propose a novel system that combines surgical gesture segmentation, surgical gesture recognition, and expertise analysis of surgical profiles in minimally invasive surgery (MIS). Kinematic analysis was used to segment gestures from a continuous motion stream. Human anatomy driven Hidden Markov Models (HMMs) are adopted for gesture recognition and expertise identification. When the proposed system was tested on a library of 200 samples for every basic surgical gesture, the gesture recognition module reported a perfect accuracy rate for the basic gestures, while the expertise identification module showed 94.7% accuracy.
AB - Surgical movement is composed of discrete gestures that are combined to perform complex surgical procedures. A promising approach to objective surgical skill evaluation systems is kinematics and kinetic analysis of hand movement that yields a gesture level analysis of proficiency of a performed movement. In this paper, we propose a novel system that combines surgical gesture segmentation, surgical gesture recognition, and expertise analysis of surgical profiles in minimally invasive surgery (MIS). Kinematic analysis was used to segment gestures from a continuous motion stream. Human anatomy driven Hidden Markov Models (HMMs) are adopted for gesture recognition and expertise identification. When the proposed system was tested on a library of 200 samples for every basic surgical gesture, the gesture recognition module reported a perfect accuracy rate for the basic gestures, while the expertise identification module showed 94.7% accuracy.
KW - Gesture recognition
KW - Surgical motion
KW - Surgical skill evaluation
UR - http://www.scopus.com/inward/record.url?scp=34547231067&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34547231067&partnerID=8YFLogxK
U2 - 10.1145/1180639.1180792
DO - 10.1145/1180639.1180792
M3 - Conference contribution
AN - SCOPUS:34547231067
SN - 1595934472
SN - 9781595934475
T3 - Proceedings of the 14th Annual ACM International Conference on Multimedia, MM 2006
SP - 719
EP - 722
BT - Proceedings of the 14th Annual ACM International Conference on Multimedia, MM 2006
T2 - 14th Annual ACM International Conference on Multimedia, MM 2006
Y2 - 23 October 2006 through 27 October 2006
ER -