Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, The Netherlands.
J Biomed Inform. 2011 Jun;44(3):455-62. doi: 10.1016/j.jbi.2010.01.004. Epub 2010 Jan 7.
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.
从传感器读数中识别和理解外科高级任务对于手术流程分析很重要。由于传感器数据的固有不确定性和手术室环境的复杂性，外科高级任务识别也是普适计算中的一项具有挑战性的任务。在本文中，我们提出了一种从低级噪声传感器数据中识别高级任务的框架。具体来说，我们提出了一种基于马尔可夫的方法，用于从一组低级传感器数据推断高级任务。我们还提出使用贝叶斯方法来清理嘈杂的传感器数据。在一个无噪声的十项手术数据集上的初步结果表明，使用检测准确率高达 90%的方法识别外科高级任务是可能的。在训练步骤之前使用清理算法可以显著提高识别精度。最后，我们强调了该领域令人兴奋的研究方向。
