Discovery of high-level tasks in the operating room

Recognizing and understanding surgical high-level tasks from sensor readings is important for surgical workflow analysis. Surgical high-level task recognition is also a challenging task in ubiquitous computing because of the inherent uncertainty of sensor data and the complexity of the operating room environment. In this paper, we present a framework for recognizing high-level tasks from low-level noisy sensor data. Specifically, we present a Markov-based approach for inferring high-level tasks from a set of low-level sensor data. We also propose to clean the noisy sensor data using a Bayesian approach. Preliminary results on a noise-free dataset of ten surgical procedures show that it is possible to recognize surgical high-level tasks with detection accuracies up to 90%. Introducing missed and ghost errors to the sensor data results in a significant decrease of the recognition accuracy. This supports our claim to use a cleaning algorithm before the training step. Finally, we highlight exciting research directions in this area.

[1]  Jianping Zeng,et al.  A new distance measure for hidden Markov models , 2010, Expert Syst. Appl..

[2]  Gari D. Clifford,et al.  Shortliffe Edward H, Cimino James J: "Biomedical Informatics; Computer Applications in Health Care and Biomedicine" , 2006 .

[3]  Daniel W. Engels.  On Ghost Reads in RFID Systems , 2005 .

[4]  Vimla L. Patel,et al.  Expertise and Tacit Knowledge in Medicine , 1999 .

[5]  Aung Aung Phyo Wai,et al.  Application of ultrasonic sensors in a smart environment , 2007, Pervasive Mob. Comput..

[6]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[7]  Alex Pentland,et al.  Human computing and machine understanding of human behavior: a survey , 2006, ICMI '06.

[8]  Jiawei Han,et al.  Cost-Conscious Cleaning of Massive RFID Data Sets , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[9]  Hee Yong Youn,et al.  Proceedings of the 10th international conference on Ubiquitous computing , 2008, UbiComp 2008.

[10]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[11]  Kees M. van Hee,et al.  Workflow Management: Models, Methods, and Systems , 2002, Cooperative information systems.

[12]  Bela Stantic,et al.  Correcting Stored RFID Data with Non-Monotonic Reasoning , 2007 .

[13]  Lawrence M. Fagan,et al.  Medical informatics: computer applications in health care and biomedicine (Health informatics) , 2003 .

[14]  Nassir Navab,et al.  On-line Recognition of Surgical Activity for Monitoring in the Operating Room , 2008, AAAI.

[15]  Gero Strauß,et al.  Acquisition of Process Descriptions from Surgical Interventions , 2006, DEXA.

[16]  Joseph A. Horvath,et al.  Tacit knowledge in professional practice : researcher and practitioner perspectives , 2000 .

[17]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[18]  Qiang Yang,et al.  Real world activity recognition with multiple goals , 2008, UbiComp.

[19]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  E. Campo,et al.  Smart house automation system for the elderly and the disabled , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.