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首页> 外文会议>International Conference on Advanced Information Systems Engineering;International on Business Process Modeling, Development and Support;International Conference on Evaluation and Modeling Methods for Systems Analysis and Development >Reference Method for the Development of Domain Action Recognition Classifiers: The Case of Medical Consultations
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Reference Method for the Development of Domain Action Recognition Classifiers: The Case of Medical Consultations

机译：开发领域行动识别分类器的参考方法：以医疗咨询为例

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摘要

Advances in human action recognition and interaction recognition enable the reliable execution of action classification tasks through machine learning algorithms. However, no systematic approach for developing such classifiers exists and since actions vary between domains, appropriate and usable datasets are uncommon. In this paper, we propose a reference method that assists non-experts in building classifiers for domain action recognition. To demonstrate feasibility, we instantiate it in a case study in the medical domain that concerns the recognition of basic actions of general practitioners. The developed classifier is effective, as it shows a prediction accuracy of 75.6% for the medical action classification task and of more than 90% for three related classification tasks. The study shows that the method can be applied to a specific activity context and that the resulting classifier has an acceptable prediction accuracy. In the future, fine-tuning of the method parameters will endorse the applicability to other domains.
机译：人类动作识别和交互识别的进步使得通过机器学习算法能够可靠地执行动作分类任务。但是，不存在用于开发此类分类器的系统方法，并且由于操作在各个域之间有所不同，因此合适且可用的数据集并不常见。在本文中，我们提出了一种参考方法，该方法可以帮助非专家构建用于域动作识别的分类器。为了证明可行性，我们在医学领域的案例研究中将其实例化，该案例研究涉及对全科医生基本行为的认可。所开发的分类器非常有效，因为它对医疗行为分类任务的预测准确性为75.6％，对三个相关分类任务的预测准确性为90％以上。研究表明，该方法可以应用于特定的活动上下文，并且所得到的分类器具有可接受的预测准确性。将来，方法参数的微调将认可其在其他领域的适用性。

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