Real-time eating monitoring: A cyber-physical systems approach

Abstract

Numerous health conditions, such as obesity, diabetes, and cardiovascular diseases, require strict adherence to nutritional guidelines and accurate reporting of eating behaviors, making effective eating monitoring essential. A common approach to eating monitoring involves maintaining a food diary, where subjects manually self-report eating events, a process inherently prone to imprecision. Recent technological advances have enabled the development of passive, automatic eating detection systems, typically relying on data from wearable devices to identify eating events. In this context, the literature is vast on efforts that use machine learning methods for this purpose, with great success. However, most existing studies focus only on eating detection mechanisms and fail to offer an integrated solution with practical use cases. To address this gap, in this work, we present a cyber–physical systems approach to eating monitoring that integrates an eating event detection module with a cloud-based service-oriented backbone where numerous services are deployed, yielding an integrated solution for real-time eating monitoring.