Navigating Weight Prediction with Diet Diary
Abstract: Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series forecasting framework. Specifically,
we introduce a Unified Meal Representation Learning (UMRL) module to extract representations for each meal. Additionally, we design a diet-aware loss function to associate food intake with weight variations. By conducting experiments on the DietDiary dataset with two state-of-the-art time series forecasting models, NLinear and iTransformer, we demonstrate that our proposed framework achieves superior performance compared to the original models. We will make our dataset, code, and models publicly available.
Primary Subject Area: [Content] Media Interpretation
Relevance To Conference: This paper introduces the DietDiary dataset, which integrates daily dietary diaries and corresponding weight measurements. We proposes a novel weight prediction task using dietary diaries and develops a model-agnostic time series forecasting framework. This includes a Unified Meal Representation Learning (UMRL) module for enhanced meal representation and a diet-aware loss function, linking multimedia food data with weight changes. This paper notably enhances the application of multimedia in health, making it highly relevant for ACM Multimedia.
Submission Number: 2044
Loading