FamilyLog:一个监控家庭用餐时间活动的移动系统。

Chongguang Bi, Guoliang Xing, Tian Hao, Jina Huh, Wei Peng, Mengyan Ma
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引用次数: 27

摘要

研究表明,家庭用餐时间在建立家庭成员之间的良好关系和保持他们的身心健康方面起着至关重要的作用。特别是,经常与家人共进晚餐可以显著降低肥胖的患病率。然而,美国有孩子的家庭在工作日平均只花1个小时在家庭聚餐上,而花3个小时看电视。细粒度的活动记录被证明对提高自我意识和激励人们改变他们的生活方式以改善健康是有效的。本文介绍了FamilyLog——一个使用智能手机和智能手表记录家庭用餐时间活动的实用系统。FamilyLog在用餐时间自动检测和记录活动的细节,包括用餐的发生和持续时间,对话,参与者,电视观看等,以不引人注目的方式。基于从真实家庭中收集的传感器数据,我们精心设计了一组复杂活动的信号特征,包括吃饭时的咔嗒声、吃东西时的手臂动作、人声、电视声音等。此外,FamilyLog通过基于hmm的分类器,将家庭中可用的多个移动设备的内置传感器数据融合在一起。为了评估FamilyLog的实际性能,我们进行了广泛的实验,包括来自8个有孩子的家庭的37名受试者的77天传感器数据。我们的研究结果表明,FamilyLog可以在不同的家庭和家庭环境中高精度地检测到这些事件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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FamilyLog: A Mobile System for Monitoring Family Mealtime Activities.

Research has shown that family mealtime plays a critical role in establishing good relationships among family members and maintaining their physical and mental health. In particular, regularly eating dinner as a family significantly reduces prevalence of obesity. However, American families with children spend only 1 hour on family meals while three hours watching TV on an average work day. Fine-grained activity-logging is proven effective for increasing self-awareness and motivating people to modify their life styles for improved wellness. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family through an HMM-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. Our results show that FamilyLog can detect those events with high accuracy across different families and home environments.

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