Decision Support for Video-based Detection of Flu Symptoms
This work addresses disease control and diagnostics for operators, but it appears incremental as it builds on existing action recognition and decision support methods.
The paper tackled the problem of detecting flu-like symptoms from video by using skeleton features for action recognition of coughing and sneezing, and proposed a decision support system with risk and trust measures to bridge machine learning and reasoning, achieving performance metrics that were evaluated but not specified with concrete numbers.
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.