FitByte: Automatic Diet Monitoring in Unconstrained Situations Using Multimodal Sensing on Eyeglasses

Bedri, Abdelkareem; Li, Diana; Khurana, Rushil; Bhuwalka, Kunal; Goel, Mayank

doi:10.1145/3313831.3376869

Cited by 61 publications

(41 citation statements)

References 36 publications

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“…The detection accuracy was 93.8%. Bedri et al developed an eyeglasses-type device "Fitbyte" to detect food intake events [5] . This device contains an inertial and optical sensor in noisy environments.…”

Section: Methods For Recognition and Analysis Of Eating Activity From Chewing Soundmentioning

confidence: 99%

Automatic Segmentation Method of Bone Conduction Sound for Eating Activity Detailed Detection

Kamachi

Kondo

Hossain

et al. 2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Many studies have shown that a low chewing rate during meals leads to obesity. Also, presenting the chewing amount in real-time to eaters prevents them from eating too fast and improves their eating awareness. This study aims to provide a system that improves the awareness of good eating habits by real-time quantification of eating activity in a natural meal environment. The concrete object of this research is to develop a method to segment automatically eating detailed activities accurately. We collected meal sound data from 14 subjects in a natural meal environment using a bone conduction microphone. Several people did data labeling, keeping only similar labels to make a robust dataset. This paper proposed a method that automatically segments the bone conduction sound corresponding to the eating detailed activities. The evaluation of precision was 88.1%, and the average recall of each class was 70.5%. CCS CONCEPTS• Ubiquitous and mobile computing → Activity detection.

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Section: Methods For Recognition and Analysis Of Eating Activity From Chewing Soundmentioning

confidence: 99%

Automatic Segmentation Method of Bone Conduction Sound for Eating Activity Detailed Detection

Kamachi

Kondo

Hossain

et al. 2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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“…reported food practice questionnaires 23,24 and surveys 26 ; for detailed food consumption records in textual 64,65,68 or photographic formats 12,60 (with the potential of data organized on a meal-by-meal basis or aggregated to provide an overview of dietary macronutrient content), information about meal duration and frequency, [52][53][54][55] and summaries of food purchasing behavior. 68,69 Integration of food practice data into the EHR is dependent on effectively bridging among a variety of platforms, standards, and methods.…”

Section: Incorporating Data Into the Electronic Health Recordmentioning

confidence: 99%

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

2022

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Background Food practice plays an important role in health. Food practice data collected in daily living settings can inform clinical decisions. However, integrating such data into clinical decision-making is burdensome for both clinicians and patients, resulting in poor adherence and limited utilization. Automation offers benefits in this regard, minimizing this burden resulting in a better fit with a patient's daily living routines, and creating opportunities for better integration into clinical workflow. Although the literature on patient-generated health data (PGHD) can serve as a starting point for the automation of food practice data, more diverse characteristics of food practice data provide additional challenges. Objectives We describe a series of steps for integrating food practices into clinical decision-making. These steps include the following: (1) sensing food practice; (2) capturing food practice data; (3) representing food practice; (4) reflecting the information to the patient; (5) incorporating data into the EHR; (6) presenting contextualized food practice information to clinicians; and (7) integrating food practice into clinical decision-making. Methods We elaborate on automation opportunities and challenges in each step, providing a summary visualization of the flow of food practice-related data from daily living settings to clinical settings. Results We propose four implications of automating food practice hereinafter. First, there are multiple ways of automating workflow related to food practice. Second, steps may occur in daily living and others in clinical settings. Food practice data and the necessary contextual information should be integrated into clinical decision-making to enable action. Third, as accuracy becomes important for food practice data, macrolevel data may have advantages over microlevel data in some situations. Fourth, relevant systems should be designed to eliminate disparities in leveraging food practice data. Conclusion Our work confirms previously developed recommendations in the context of PGHD work and provides additional specificity on how these recommendations apply to food practice.

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“…This study investigates how feedback could be provided that will help the user make healthier choices during their meal. Researches also investigated eating monitoring technologies, such as using eyeglasses to automatically monitor the diet [12] or using a multi-sensor necklace for detecting eating [167]. These sensing technologies make it achievable to provide real time intervention.…”

Section: Motivationmentioning

confidence: 99%

“…However, many of these interventions are relatively difficult to apply in everyday life. For example, some require elaborate experimental settings in a laboratory [17] while others require setting up extra devices and equipment such as a special scale 12 . Such rather burdensome tools and settings might discourage improvements in eating habits.…”

Section: O N C L U S I O Nmentioning

confidence: 99%

A smart utensil for detecting food pick-up gesture and amount while eating

Zhang

Zheng

Rempel

et al. 2020

Proceedings of the 11th Augmented Human International Conference

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Seemingly trivial eating habits, such as eating too fast, have been linked to diverse and rather serious health issues. While technology-mediated interventions have leveraged several strategies to promote healthy dietary habits, designing successful pervasive interventions remains challenging. This thesis first offers three contributions with the aim of assisting in the design of eating interventions: 1) a review of 62 studies which focused on interventions targeting eating habits; 2) a generative design framework with multiple design parameters; and, 3) an exploration of the potential efficacy of the developed framework. These contributions will improve

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FitByte: Automatic Diet Monitoring in Unconstrained Situations Using Multimodal Sensing on Eyeglasses

Abstract: Figure 1: FitByte was trained and validated using data collected in five unconstrained situations: (from left to right) in a lunch meeting, watching TV, grabbing and consuming a quick snack from a cafe, exercising in a gym, and hiking outdoors.

Cited by 61 publications

References 36 publications

Automatic Segmentation Method of Bone Conduction Sound for Eating Activity Detailed Detection

Automatic Segmentation Method of Bone Conduction Sound for Eating Activity Detailed Detection

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

A smart utensil for detecting food pick-up gesture and amount while eating

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