Discovery of high-level tasks in the operating room

被引:55
作者
Bouarfa, L. [1 ]
Jonker, P. P. [1 ]
Dankelman, J. [1 ]
机构
[1] Delft Univ Technol, Dept Biomech Engn, NL-2628 CD Delft, Netherlands
关键词
Ubiquitous computing; Activity recognition; High-level activity; Cognitive environment; Hidden Markov Model; Surgical workflow; Noisy sensors; Uncertainty; Bayesian networks;
D O I
10.1016/j.jbi.2010.01.004
中图分类号
TP39 [计算机的应用];
学科分类号
080201 [机械制造及其自动化];
摘要
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. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码:455 / 462
页数:8
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