Discovery of high-level tasks in the operating room
Discovery of high-level tasks in the operating room
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DOI：
10.1016/j.jbi.2010.01.004
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发表时间：
2011-06-01
影响因子：
4.5
通讯作者：
Dankelman, J.
中科院分区：
文献类型：
--
作者：
Bouarfa, L.;Jonker, P. P.;Dankelman, J.
从传感器读数中识别和理解手术高级任务对于手术工作流程分析非常重要。由于传感器数据固有的不确定性和手术室环境的复杂性，手术高级任务识别也是普适计算中具有挑战性的任务。在本文中，我们提出了一个框架，从低层次的噪声传感器数据识别高层次的任务。具体来说，我们提出了一个基于马尔可夫的方法来推断高层次的任务，从一组低层次的传感器数据。我们还建议使用贝叶斯方法来清理噪声传感器数据。对10个外科手术的无噪声数据集的初步结果表明，有可能识别出检测准确率高达90%的外科高级任务。引入错过和幽灵错误的传感器数据的识别精度显着下降的结果。这支持了我们在训练步骤之前使用清洗算法的主张。最后，我们强调了这一领域令人兴奋的研究方向。(C)2010年爱思唯尔公司All rights reserved.
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.