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Monitoring Dietary Behavior with a Smart Dining Tray

机译:使用智能餐盘监控饮食行为

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In this article, the authors investigate a new source of information for dietary monitoring: pressure distribution on the surface underneath dining plates. Pressure sensing has been used to consider the weight of the eaten food. The core idea behind their work is that dynamic pressure information can also be used to distinguish between various cutlery-related activities, such as cutting, poking, stirring, or scooping. The authors show how to spot such individual actions in continuous datastreams, assign them to specific containers (main plate, salad bowl), count them (how many bites taken), and relate them to different abstract food categories. They consider two sensing modalities: (1) textile pressure-sensor matrix technology facilitating a "smart tablecloth" that looks and feels like a standard tablecloth but provides detailed information on the spatial and temporal pressure; and (2) standard force sensitive resistor (FSR) sensors placed underneath a rigid tray. They also present the results of a new, comprehensive study with 10 subjects, each of whom consumed a total of eight meals chosen from 17 possible main dishes with six possible side dishes; results show an average accuracy of up to 94 percent. This article is part of a special issue on pervasive food.
机译:在本文中,作者研究了饮食监测的新信息来源:餐盘下方表面的压力分布。压力感测已被用于考虑食用食物的重量。他们工作背后的核心思想是动态压力信息还可用于区分各种餐具相关活动,例如切割,戳戳,搅动或铲起。作者展示了如何在连续的数据流中发现这些单独的动作,如何将它们分配到特定的容器(主板,色拉碗),对其进行计数(被咬的次数),并将它们与不同的抽象食品类别相关联。他们考虑了两种传感方式:(1)纺织压力传感器矩阵技术,有助于“智能桌布”的外观和感觉像是标准桌布,但提供了有关时空压力的详细信息; (2)标准力敏电阻器(FSR)传感器放置在刚性托盘下面。他们还介绍了一项新的,全面的研究结果,涉及10位受试者,每位受试者总共从17种可能的主菜和6种可能的配菜中选择了8种。结果显示平均精度高达94%。本文是有关普及食品的特刊的一部分。

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