Wearable Eating Habit Sensing System Using Internal Body Sound

Shimizu, Masaki; Komori, Shintaro; Takashima, Tomoko; Lopez, Guillaume; Tatsuta, Seiji; Yanagimoto, Shintaro; Warisawa, Shin’ichi; Delaunay, Jean‐Jacques; Yamada, Isao

doi:10.1299/jamdsm.4.158

Cited by 58 publications

(48 citation statements)

References 8 publications

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“…Similarly, Pabler et al [83] took advantage of chewing sound in the breakdown process detected by the sensor system in the outer ear canal. Other studies that are likewise capable of detecting chewing events with the count numbers of chewing and food texture classification are proposed in [84,85].…”

Section: Acoustic Approachmentioning

confidence: 99%

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

et al. 2017

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Wearable devices monitoring food intake through passive sensing is slowly emerging to complement self-reporting of users' caloric intake and eating behaviors. Though the ultimate goal for the passive sensing of eating is to become a reliable gold standard in dietary assessment, it is currently showing promise as a means of validating self-report measures. Continuous food-intake monitoring allows for the validation and refusal of users' reported data in order to obtain more reliable user information, resulting in more effective health intervention services. Recognizing the importance and strength of wearable sensors in food intake monitoring, there has been a variety of approaches proposed and studied in recent years. While existing technologies show promise, many challenges and opportunities discussed in this survey, still remain. This paper presents a meticulous review of the latest sensing platforms and data analytic approaches to solve the challenges of food-intake monitoring, ranging from ear-based chewing and swallowing detection systems that capture eating gestures to wearable cameras that identify food types and caloric content through image processing techniques. This paper focuses on the comparison of different technologies and approaches that relate to user comfort, body location, and applications for medical research. We identify and summarize the forthcoming opportunities and challenges in wearable food intake monitoring technologies.

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Section: Acoustic Approachmentioning

confidence: 99%

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

et al. 2017

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“…He concluded that chewing of each food type generated characteristic sounds. Based on this, several research groups developed devices and systems to record food intake sounds: [6], [7], [11]- [14]. These systems were small, body worn devices.…”

Section: A Studies On Automated Food Intake Monitoringmentioning

confidence: 99%

“…As the foam of the earpad at least partly occluded the outer ear canal of the user, it reduced the natural hearing ability when worn. Bone conduction microphones placed in the outer ear canal were used to record chewing sounds by Shuzo et al [7]. Recordings were stored in a wearable sound recorder.…”

Section: B Studies On Chewing Sound Recognitionmentioning

confidence: 99%

“…For a wearable solution, an algorithm for automated chew event detection with high precision and recall is needed. Several research groups have already developed algorithms for detecting chew events: [5]- [7]. In this paper, we evaluated the performance of different algorithms for this task on our own datasets of food intake sound recordings.…”

Section: Introductionmentioning

confidence: 99%

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Food Intake Monitoring: Automated Chew Event Detection in Chewing Sounds

Päßler

Fischer

2014

IEEE J. Biomed. Health Inform.

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The analysis of the food intake behavior has the potential to provide insights into the development of obesity and eating disorders. As an elementary part of this analysis, chewing strokes have to be detected and counted. Our approach for food intake analysis is the evaluation of chewing sounds generated during the process of eating. These sounds were recorded by microphones applied to the outer ear canal of the user. Eight different algorithms for automated chew event detection were presented and evaluated on two datasets. The first dataset contained food intake sounds from the consumption of six types of food. The second dataset consisted of recordings of different environmental sounds. These datasets contained 68,094 chew events in around 18 h recording data. The results of the automated chew event detection were compared to manual annotations. Precision and recall over 80% were achieved by most of the algorithms. A simple noise reduction algorithm using spectral subtraction was implemented for signal enhancement. Its benefit on the chew event detection performance was evaluated. A reduction of the number of false detections by 28% on average was achieved by maintaining the detection performance. The system is able to be used for calculation of the chewing frequency in laboratory settings.

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“…Shuzo et al utilized structured audio lifelogs to support future life [9]. They analyzed sounds during mealtimes.…”

Section: Researches Using Audio Lifelogsmentioning

confidence: 99%

Utterance Rate Feedback for Enhancing Mealtime Communication

Ogawa

Takeuchi

Nishimura

et al. 2011

2011 IEEE International Symposium on Multimedia

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The purpose of this research is to support users' mealtime communication by changing their utterance rate during meals. By logging and analyzing the utterance rate, or the ratio of utterance length to mealtime length, we determined the relationship between the utterance rate and the distribution of the community to which each meal companion belonged. Using this relationship, we developed a real-time feedback system that presented a pre-estimated utterance rate with rate at the present time during a meal. This utterance rate was estimated according to logged utterance rate data and input data about members at the table. In order to evaluate our system, we asked six users to use it. As a result, we found out that users almost always wanted to increase the utterance rate and users' utterance rates increased as they wanted after using our feedback system.

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Wearable Eating Habit Sensing System Using Internal Body Sound

Cited by 58 publications

References 8 publications

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

Food Intake Monitoring: Automated Chew Event Detection in Chewing Sounds

Utterance Rate Feedback for Enhancing Mealtime Communication

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