Toward Real-time, High-performance, and Generalizable Eating Episode Detection and Postprandial Carbohydrate Content Classification Using Non-invasive Wearables

Abstract

As society grapples with the rising prevalence of diet-related diseases such as type 2 diabetes and coronary heart disease, the need for effective dietary monitoring becomes increasingly critical. Traditional self-reporting approaches such as 24-hour dietary recalls and food frequency questionnaires, while considered gold standard approaches, are plagued by high costs, significant memory demands on users, and inaccuracies, rendering them less than ideal for addressing the current health crisis. This reality has spurred the development of innovative dietary monitoring techniques, resulting in the advent of Automatic Dietary Monitoring (ADM) systems. These systems are designed to automatically track critical aspects of food intake, including the timing and duration of meals, the quantity of food consumed, and its nutritional content.This dissertation investigates the development of a real-time, high-performance, and generalizable eating detection platform using heart rate data from non-invasive wearables to detect eating episodes when an individual is sitting down. Additionally, it delves into the development of algorithms capable of classifying the carbohydrate content in foods using heart rate data from medical-grade, non-invasive wearables.We developed timeStampr—an iOS application for collecting timestamps essential for data labeling and ground truth establishment. We collected heart rate data from 23 participants in a controlled yet naturalistic laboratory setting using an Empatica E4 worn on the upper arm while individuals were eating. From the initial cohort, we excluded data from three participants due to sensor irregularities with dark skin tones and failure to meet the study’s health criteria.Our classifiers exhibited robust performance within a 90-second window, with the eating detection model achieving at least 87% in accuracy, precision, recall, and AUC-ROC, while the carbohydrate content model attained a minimum of 84% across these same metrics, all utilizing heart rate data from an Empatica E4. Additionally, this work demonstrates real-time testing of predictive models through RESTful APIs. Overall, the results of this dissertation demonstrate the potential of heart rate in eating detection and carbohydrate content classification.