Print Email Facebook Twitter Discovery of high-level tasks in the operating room Title Discovery of high-level tasks in the operating room Author Bouarfa, L. Jonker, P.P. Dankelman, J. Faculty Mechanical, Maritime and Materials Engineering Department Biomechanical Engineering Date 2010-01-07 Abstract 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. Subject ubiquitous computingactivity recognitionhigh-level activitycognitive environmentHidden Markov Modelsurgical workflownoisy sensorsuncertaintyBayesian networks To reference this document use: http://resolver.tudelft.nl/uuid:eb90135c-36aa-4de0-8057-c4d026abcf42 DOI https://doi.org/10.1016/j.jbi.2010.01.004 Publisher Elsevier ISSN 1532-0464 Source Journal of Biomedical Informatics, 44 (3), 2011 Part of collection Institutional Repository Document type journal article Rights (c) 2010 Elsevier Files PDF Bouarfa_2011.pdf 942.59 KB Close viewer /islandora/object/uuid:eb90135c-36aa-4de0-8057-c4d026abcf42/datastream/OBJ/view