Predicting "About-to-Eat" Moments for Just-in-Time Eating Intervention

Various wearable sensors capturing body vibration, jaw movement, hand gesture, etc., have shown promise in detecting when one is currently eating. However, based on existing literature and user surveys conducted in this study, we argue that a Just-in-Time eating intervention, triggered upon detecting a current eating event, is sub-optimal. An eating intervention triggered at "About-to-Eat" moments could provide users with a further opportunity to adopt a better and healthier eating behavior. In this work, we present a wearable sensing framework that predicts "About-to-Eat"moments and the "Time until the Next Eating Event". The wearable sensing framework consists of an array of sensors that capture physical activity, location, heart rate, electrodermal activity, skin temperature and caloric expenditure. Using signal processing and machine learning on this raw multimodal sensor stream, we train an "Aboutto- Eat" moment classifier that reaches an average recall of 77%. The "Time until the Next Eating Event" regression model attains a correlation coefficient of 0.49. Personalization further increases the performance of both of the models to an average recall of 85% and correlation coefficient of 0.65. The contributions of this paper include user surveys related to this problem, the design of a system to predict about to eat moments and a regression model used to train multimodal sensory data in real time for potential eating interventions for the user.

各种可穿戴传感器能够捕捉身体振动、下颌运动、手势等信息，在检测一个人当前是否正在进食方面显示出了潜力。然而，根据现有文献以及本研究中进行的用户调查，我们认为，在检测到当前进食事件时触发的即时进食干预并非最佳选择。在“即将进食”时刻触发的进食干预能够为用户提供进一步的机会，使其采取更好、更健康的进食行为。在这项工作中，我们提出了一种可穿戴传感框架，该框架能够预测“即将进食”时刻以及“到下一次进食事件的时间”。可穿戴传感框架由一系列传感器组成，这些传感器能够捕捉身体活动、位置、心率、皮肤电活动、皮肤温度和热量消耗。通过对原始的多模态传感器数据流进行信号处理和机器学习，我们训练了一个“即将进食”时刻分类器，其平均召回率达到77%。“到下一次进食事件的时间”回归模型的相关系数达到0.49。个性化进一步将这两个模型的性能提高到平均召回率85%和相关系数0.65。本文的贡献包括与该问题相关的用户调查、一个预测即将进食时刻的系统设计以及一个用于实时训练多模态感官数据以便为用户进行潜在进食干预的回归模型。