A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study.
A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study.
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DOI：
10.2196/20625
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发表时间：
2020-12-18
影响因子：
5
通讯作者：
Abowd GD
中科院分区：
文献类型：
--
作者：
Bin Morshed M;Kulkarni SS;Li R;Saha K;Roper LG;Nachman L;Lu H;Mirabella L;Srivastava S;De Choudhury M;de Barbaro K;Ploetz T;Abowd GD
饮食行为对一个人的健康有很大的影响。这种行为不仅涉及到一个人什么时候吃东西，还涉及到各种环境因素，比如和谁一起吃，在哪里吃，吃的是什么食物。尽管这些因素具有相关性，但大多数自动进食检测系统的设计并不是为了捕捉环境因素。本研究的目的是：(1)设计并构建一个基于智能手表的饮食检测系统，该系统可以根据主要手部运动来检测用餐事件；(2)设计生态瞬间评估（EMA）问题，以便在饮食检测系统检测到用餐事件时捕捉用餐上下文；(3)验证在被动检测用餐事件时触发EMA问题的用餐检测系统。在为期三周的时间里，美国一所大学的28名大学生使用了这种膳食检测系统。当进食检测系统正确检测到进食事件时，参与者通过EMAs报告各种上下文数据。EMA的问题是在对来自同一校区的162名学生进行调查研究后设计的。ema的反应被用来确定排除标准。在总膳食中，早餐（264/294）、午餐（406/410）和晚餐(98.0%（589/601）被我们的新型膳食检测系统检测到。进食检测系统的准确率为96.48%（1259/1305）。该分类器的准确率为80%，召回率为96%，F1为87.3%。我们发现超过99%（1248/1259）的检测到的食物是在分心的情况下消耗的。这样的饮食行为被认为是“不健康的”，会导致暴饮暴食和不受控制的体重增加。单独用餐的比例较高（680/1259,54.01%）。我们的参与者自我报告62.98%（793/1259）的膳食是健康的。总之，这些结果对设计鼓励健康饮食行为的技术具有启示意义。提出的饮食检测系统是同类中第一个利用EMAs来捕捉饮食环境的系统，这对健康研究具有重要意义。我们对系统收集的背景数据进行了反思，并讨论了如何使用这些见解来设计针对个人的干预措施。
Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered “unhealthy” and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.