Decision Support for Video-based Detection of Flu Symptoms

TL;DR

This paper presents a decision support system that uses video-based skeleton analysis and machine learning to detect flu symptoms like coughing and sneezing, aiding disease control efforts.

Contribution

It introduces a novel combination of a residual temporal convolutional network and causal networks for action recognition and decision support in flu detection.

Findings

Proposed network achieves high accuracy in action recognition.

Risk and trust metrics effectively integrate machine learning outputs into decision-making.

System demonstrates potential for real-time flu symptom detection.

Abstract

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.