Simulation Interface for Gesture-Based Remote Control of a Surgical Lighting Arm

Asepsis preservation is compulsory in operating rooms (ORs) for avoiding the spread of hospital-acquired diseases. It is enforced nowadays by drastic measures regulating the use of sterile devices by non-sterile staff and vice versa. Contact less computer vision-based interfaces may overcome such limitations. Our works relates to the development of an image processing chain aiming at giving surgeons remote control over OR equipment. In this paper, we introduce a simulation of a gesture-based remote control interface intended for ORs. The software processing chain and the choices made for each of its steps are described, and its performance is quantified. A virtual scene including a robotic lighting support is introduced. A graphical interface enables the coupling of both processing chain and virtual scene, enabling the user to control the virtual lighting support by performing various hand postures. Functionalities include 3-dimensional displacements, control of the lighting intensity and task-specific pre-programmed movements.

[1]  Prospero C. Naval,et al.  REALISM: Real-Time Hand Gesture Interface for Surgeons and Medical Experts , 2009 .

[2]  Ulrich G. Hofmann,et al.  Touch- and marker-free interaction with medical software , 2009 .

[3]  T Neumuth,et al.  [Intraoperative use of electronic patient records by the surgeon. An evaluation of the "MI Report" in ENT surgery]. , 2011, HNO.

[4]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[5]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[6]  Hélène Laurent,et al.  Hand gesture recognition using a dedicated geometric descriptor , 2012, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA).

[7]  J. Denavit,et al.  A kinematic notation for lower pair mechanisms based on matrices , 1955 .

[8]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[9]  H. Stern,et al.  A gesture-based tool for sterile browsing of radiology images. , 2008, Journal of the American Medical Informatics Association : JAMIA.

[10]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[11]  V. Pisarevsky,et al.  Intel's Computer Vision Library: applications in calibration, stereo segmentation, tracking, gesture, face and object recognition , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[12]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[13]  I. Ahmad,et al.  PREVALENCE OF NOSOCOMIAL INFECTIONS IN SURGICAL WARDS OF TERTIARY CARE HOSPITAL AT LUCKNOW , 2012 .

[14]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[15]  Hélène Laurent,et al.  Vision-Based System for Human Detection and Tracking in Indoor Environment , 2010, Int. J. Soc. Robotics.

[16]  NHS Scotland National Healthcare Associated Infection (HAI) Prevalence Survey Report released , 2007 .

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[18]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[19]  Wisama Khalil,et al.  Modeling, Identification and Control of Robots , 2003 .

[20]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[21]  Jean-Michel Jolion,et al.  Object count/area graphs for the evaluation of object detection and segmentation algorithms , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[22]  R. S. Jadon,et al.  A REVIEW OF VISION BASED HAND GESTURES RECOGNITION , 2009 .