论文标题
数据驱动的端到端方法，用于使用智能手表对饮食行为进行野外监控
A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches
论文作者
论文摘要
肥胖症的全球流行率提高，激发了科学界的利益，可以客观地自动监测饮食行为。尽管研究了肥胖症，但此类工具也可以用于研究饮食失调（例如神经性厌食症）或为患者或运动员提供个性化的监测平台。本文为自动化的i）介绍了一个完整的框架i）对餐内饮食行为的建模和ii）餐食的时间定位，从使用市售的智能手表收集的原始惯性数据。最初，我们提出了一个端到端的神经网络，该神经网络检测食物摄入事件（即叮咬）。提出的网络同时使用同时训练的卷积和经常性层。随后，我们展示了如何使用信号处理算法全天使用检测到的叮咬的分布来估计餐点和终点。我们对每个框架部分进行广泛的评估。一对一的受试者（LOSO）评估表明，我们的咬合检测方法在进餐过程中比检测叮咬的四种最先进的算法（0.923 F1得分）。此外，有关餐点/终点估计的LOSO和HEART OUT设定实验表明，所提出的方法的表现优于文献中发现的相关方法（分别为LOSO和HOLDOUT实验的Jaccard指数为0.820和0.821）。实验是使用我们公开提供的FIC和新引入的FreeFIC数据集进行的。
The increased worldwide prevalence of obesity has sparked the interest of the scientific community towards tools that objectively and automatically monitor eating behavior. Despite the study of obesity being in the spotlight, such tools can also be used to study eating disorders (e.g. anorexia nervosa) or provide a personalized monitoring platform for patients or athletes. This paper presents a complete framework towards the automated i) modeling of in-meal eating behavior and ii) temporal localization of meals, from raw inertial data collected in-the-wild using commercially available smartwatches. Initially, we present an end-to-end Neural Network which detects food intake events (i.e. bites). The proposed network uses both convolutional and recurrent layers that are trained simultaneously. Subsequently, we show how the distribution of the detected bites throughout the day can be used to estimate the start and end points of meals, using signal processing algorithms. We perform extensive evaluation on each framework part individually. Leave-one-subject-out (LOSO) evaluation shows that our bite detection approach outperforms four state-of-the-art algorithms towards the detection of bites during the course of a meal (0.923 F1 score). Furthermore, LOSO and held-out set experiments regarding the estimation of meal start/end points reveal that the proposed approach outperforms a relevant approach found in the literature (Jaccard Index of 0.820 and 0.821 for the LOSO and heldout experiments, respectively). Experiments are performed using our publicly available FIC and the newly introduced FreeFIC datasets.
