论文标题
对基于视频的流感症状检测的决策支持
Decision Support for Video-based Detection of Flu Symptoms
论文作者
论文摘要
决策支持系统的发展是一个不断增长的领域，可以应用于疾病控制和诊断领域。使用基于视频的监视数据，提取骨架功能以执行动作识别，特别是对咳嗽和打喷嚏运动的检测和识别。提供类似流感症状的证据，基于因果网络的决策支持系统能够为操作员提供重要的信息以进行决策。提出了修改的残留时间卷积网络，使用骨架功能进行动作识别。本文介绍了使用机器学习模型的结果作为认知决策支持系统的证据的能力。我们提出风险和信任措施作为指标，以在机器学习和机器 - 策划之间桥接。我们提供了评估拟议网络性能以及如何将这些绩效指标与产生信任的风险结合使用的实验。
The development of decision support systems is a growing domain that can be applied in the area of disease control and diagnostics. Using video-based surveillance data, skeleton features are extracted to perform action recognition, specifically the detection and recognition of coughing and sneezing motions. Providing evidence of flu-like symptoms, a decision support system based on causal networks is capable of providing the operator with vital information for decision-making. A modified residual temporal convolutional network is proposed for action recognition using skeleton features. This paper addresses the capability of using results from a machine-learning model as evidence for a cognitive decision support system. We propose risk and trust measures as a metric to bridge between machine-learning and machine-reasoning. We provide experiments on evaluating the performance of the proposed network and how these performance measures can be combined with risk to generate trust.
