Evaluation of a Smart Fork to Decelerate Eating Rate

Hermsen, Sander; Frost, Jeana; Robinson, Eric; Higgs, Suzanne; Mars, Monica; Hermans, Roel C.J.

doi:10.1016/j.jand.2015.11.004

Cited by 28 publications

(24 citation statements)

References 19 publications

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“…RUNNING HEAD: Effect of feedback on eating rate, satiation, and food intake 5 gentle vibration in the handle of the fork. Although previous research indicates that the fork is perceived as a comfortable, accurate, and effective method to decelerate eating rate (Hermsen, Frost, Robinson, Higgs, Mars, & Hermans, 2016), it is still unclear whether vibrotactile feedback affects users' subsequent eating behavior. To examine this question, we conducted an experiment in which the real-time vibrotactile feedback of the fork was manipulated (i.e., vibrotactile feedback versus no feedback).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

The effect of real-time vibrotactile feedback delivered through an augmented fork on eating rate, satiation, and food intake

et al. 2017

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Eating rate is a basic determinant of appetite regulation, as people who eat more slowly feel sated earlier and eat less. Without assistance, eating rate is difficult to modify due to its automatic nature. In the current study, participants used an augmented fork that aimed to decelerate their rate of eating. A total of 114 participants were randomly assigned to the Feedback Condition (FC), in which they received vibrotactile feedback from their fork when eating too fast (i.e., taking more than one bite per 10 s), or a Non-Feedback Condition (NFC). Participants in the FC took fewer bites per minute than did those in the NFC. Participants in the FC also had a higher success ratio, indicating that they had significantly more bites outside the designated time interval of 10 s than did participants in the NFC. A slower eating rate, however, did not lead to a significant reduction in the amount of food consumed or level of satiation. These findings indicate that real-time vibrotactile feedback delivered through an augmented fork is capable of reducing eating rate, but there is no evidence from this study that this reduction in eating rate is translated into an increase in satiation or reduction in food consumption. Overall, this study shows that real-time vibrotactile feedback may be a viable tool in interventions that aim to reduce eating rate. The long-term effectiveness of this form of feedback on satiation and food consumption, however, awaits further investigation.

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Section: Accepted Manuscriptmentioning

confidence: 99%

The effect of real-time vibrotactile feedback delivered through an augmented fork on eating rate, satiation, and food intake

et al. 2017

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“…Although the annotation of human experts can be really accurate, the annotation procedure is time-consuming and prone to introduce errors due to the repetitive nature of the task. Thus, the need for the automation of the procedure has often been emphasized by experts in the field [1] [6] in order to overcome these problems and speed up the bite detection procedure. In the past several methodologies have been proposed to achieve that, with their majority being based on weight, inertial, motion and visual sensors that facilitate the recognition of bite instances [7].…”

Section: Introductionmentioning

confidence: 99%

A Deep Network for Automatic Video-Based Food Bite Detection

Konstantinidis¹,

Dimitropoulos²,

Ioakimidis³

et al. 2019

Lecture Notes in Computer Science

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Past research has now provided compelling evidence pointing towards correlations among individual eating styles and the development of (un)healthy eating patterns, obesity and other medical conditions. In this setting, an automatic, noninvasive food bite detection system can be a really useful tool in the hands of nutritionists, dietary experts and medical doctors in order to explore real-life eating behaviors and dietary habits. Unfortunately, the automatic detection of food bites can be challenging due to occlusions between hands and mouth, use of different kitchen utensils and personalized eating habits. On the other hand, although accurate, manual bite detection is time-consuming for the annotator, making it infeasible for large scale experimental deployments or real-life settings. To this regard, we propose a novel deep learning methodology that relies solely on human body and face motion data extracted from videos depicting people eating meals. The purpose is to develop a system that can accurately, robustly and automatically identify food bite instances, with the long-term goal to complement or even replace manual bite-annotation protocols currently in use. The experimental results on a large dataset reveal the superb classification performance of the proposed methodology on the task of bite detection and paves the way for additional research on automatic bite detection systems.

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“…As for other devices that can positively intervene in human eating habits, Hermsen et al (2016) carried out an experiment involving a smart fork (i.e., a fork-shaped device augmented with sensors and actuators). The fork can provide real-time haptic and visual feedback to the user (Kadomura et al, 2013), for example, producing alerts if the user eats too quickly.…”

Section: Dedicated Sensing Devicesmentioning

confidence: 99%

Computational Commensality: From Theories to Computational Models for Social Food Preparation and Consumption in HCI

et al. 2019

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Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as "commensality," positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of "Computational Commensality," that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food-and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieving Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective.

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Evaluation of a Smart Fork to Decelerate Eating Rate

Cited by 28 publications

References 19 publications

The effect of real-time vibrotactile feedback delivered through an augmented fork on eating rate, satiation, and food intake

The effect of real-time vibrotactile feedback delivered through an augmented fork on eating rate, satiation, and food intake

A Deep Network for Automatic Video-Based Food Bite Detection

Computational Commensality: From Theories to Computational Models for Social Food Preparation and Consumption in HCI

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