Human-in-the-loop learning for personalized diet monitoring from unstructured mobile data

Lifestyle interventions with the focus on diet are crucial in self-management and prevention of many chronic conditions, such as obesity, cardiovascular disease, diabetes, and cancer. Such interventions require a diet monitoring approach to estimate overall dietary composition and energy intake. Although wearable sensors have been used to estimate eating context (e.g., food type and eating time), accurate monitoring of dietary intake has remained a challenging problem. In particular, because monitoring dietary intake is a self-administered task that involves the end-user to record or report their nutrition intake, current diet monitoring technologies are prone to measurement errors related to challenges of human memory, estimation, and bias. New approaches based on mobile devices have been proposed to facilitate the process of dietary intake recording. These technologies require individuals to use mobile devices such as smartphones to record nutrition intake by either entering text or taking images of the food. Such approaches, however, suffer from errors due to low adherence to technology adoption and time sensitivity to the dietary intake context. In this article, we introduce EZNutriPal,1 an interactive diet monitoring system that operates on unstructured mobile data such as speech and free-text to facilitate dietary recording, real-time prompting, and personalized nutrition monitoring. EZNutriPal features a natural language processing unit that learns incrementally to add user-specific nutrition data and rules to the system. To prevent missing data that are required for dietary monitoring (e.g., calorie intake estimation), EZNutriPal devises an interactive operating mode that prompts the end-user to complete missing data in real-time. Additionally, we propose a combinatorial optimization approach to identify the most appropriate pairs of food names and food quantities in complex input sentences. We evaluate the performance of EZNutriPal using real data collected from 23 human subjects who participated in two user studies conducted in 13 days each. The results demonstrate that EZNutriPal achieves an accuracy of 89.7% in calorie intake estimation. We also assess the impacts of the incremental training and interactive prompting technologies on the accuracy of nutrient intake estimation and show that incremental training and interactive prompting improve the performance of diet monitoring by 49.6% and 29.1%, respectively, compared to a system without such computing units.