Cloud-based Low-Cost Smart Care: Breathing Test Radar

• Published in: International Conference on Big Data and Smart Computing pp. 325 - 328
• Main Authors: Lee, C. Alisdair, Lau, C. Francis, Chan, H. Anthony
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.02.2024
• Subjects: COVID-19; Electronic Olfaction System; Graph Convolutional Network; Healthcare; Medical services; Radar; Representation learning; Scalability; Sensitivity; Time-differential Multiple-sensor; Volatile organic compounds
• ISSN: 2375-9356
• DOI: 10.1109/BigComp60711.2024.00058

The current health care adopts smart care driven by data, utilizing multiple-sensor measurements. However, it is not straightforward how one may map the relationship of sensors using traditional machine learning methods alone. This paper introduces a method integrating a Graph Convolutional Network (GCN) with an odor-sensing array to extract the change in odor from respiratory information such as concentrations of Volatile Organic Compounds. This approach measures the differences in odor under different conditions of the subjects (e.g., 1. before and after exercise, 2. during COVID-19 sickness and after recovery) by learning the increasing concentration of gas mixtures from multiple sensors. GCN grasps the relationship between odor sensors' sensitivity and achieves an experimental accuracy rate of 81.6%. Since the graph structure is a scalable permutable domain, other odor-gain labels can potentially form a new feature learning based on this pivot feature learning.