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.
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