Understanding food consumption patterns and contexts using mobile sensing is fundamental to build mobile health applications that require minimal user interaction to generate mobile food diaries. Many available mobile food diaries, both commercial and in research, heavily rely on self-reports, and this dependency limits the long term adoption of these apps by people. The social context of eating (alone, with friends, with family, with a partner, etc.) is an important self-reported feature that influences aspects such as food type, psychological state while eating, and the amount of food, according to prior research in nutrition and behavioral sciences. In this work, we use two datasets regarding the everyday eating behavior of college students in two countries, namely Switzerland (N_ch=122) and Mexico (N_mx=84), to examine the relation between the social context of eating and passive sensing data from wearables and smartphones. Moreover, we design a classification task, namely inferring eating-alone vs. eating-with-others episodes using passive sensing data and time of eating, obtaining accuracies between 77% and 81%. We believe that this is a first step towards understanding more complex social contexts related to food consumption using mobile sensing.

中文翻译：

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一个人还是与其他人在一起？通过移动感应了解大学生的饮食情节

使用移动感应了解食品消费模式和环境是构建移动健康应用程序的基础，该应用程序需要最少的用户交互才能生成移动食品日记。商业和研究中许多可用的移动食品日记都严重依赖自我报告，这种依赖性限制了人们长期使用这些应用程序。饮食的社会背景（单独，与朋友，与家人，与伴侣等）是一项重要的自我报告功能，会影响诸如食物类型，进食时的心理状态以及食物数量等方面。营养与行为科学研究。在这项工作中，我们使用两个数据集，分别涉及瑞士（N_ch = 122）和墨西哥（N_mx = 84）这两个国家的大学生的日常饮食行为，检查饮食的社交环境与可穿戴设备和智能手机的被动感知数据之间的关系。此外，我们设计了一个分类任务，即使用被动感测数据和进食时间来推断单独进食与其他人进食的情节，得出的准确度在77％至81％之间。我们认为，这是通过移动感应了解与食物消费相关的更复杂的社会环境的第一步。