Classification Method of Eating Behavior by Dietary Sound Collected in Natural Meal Environment

Kamachi, Haruka; Kondo, Takumi; Yokokubo, Anna; Lopez, Guillaume

doi:10.1007/978-981-15-8944-7_9

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…Though the accuracy of chewing behavior was 91%, and utterance length was 96%, the performances were not evaluated in a natural meal environment. Later, we also suggested a classification method of detailed eating behavior using a bone conduction microphone in a natural meal environment [9,11,12,17] . The subjects were required to eat in the usual meal environment and freely.…”

Section: Methods For Recognition and Analysis Of Eating Activity Using Bone Conduction Soundmentioning

confidence: 99%

“…The subjects were required to eat in the usual meal environment and freely. They classified four eating activities: eating, swallowing, speaking, and other like noise [11,12] and five activities: eating, swallowing food, swallowing drink, speaking, and other like noise [9,17]. The F1 score for chewing, speaking, swallowing food, swallowing drink, and other sounds were respectively 96%, 94%, 81%, 76%, and 88%.…”

Section: Methods For Recognition and Analysis Of Eating Activity Using Bone Conduction Soundmentioning

confidence: 99%

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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 Using Bone Conduction Soundmentioning

confidence: 99%

Section: Methods For Recognition and Analysis Of Eating Activity Using Bone Conduction 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

Self Cite

View full text Add to dashboard Cite

show abstract

“…In our previous studies [14,18,19,22], we also proposed a method that uses a cheap and small bone conduction microphone to automatically monitor eating behaviors, and provide real-time feedback to increase chewing self-awareness.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Eating Activity using Smartwatch

Kamachi

Hossain

Tokuyama

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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Eating activity understanding has been extensively studied for its importance in our lives. Due to the lack of proper dietary management, obesity has been increasing worldwide, which causes many diseases. In this regard, adequate diet management is crucial for having a healthier life. This study aims to develop a system that allows users to manage their diet easily. This research proposed a method that estimates eating activities. The main objective of this research is to use an eating manner (way of eating) to identify more accurately the eating activity by arm's acceleration using a smartwatch. The acceleration data were collected using a smartwatch during different mealtime. A total of 12 features are extracted from this data. We have examined different classifiers' performance. Among them, the Support Vector Machine (cubic SVM) classifier performed best among all. An evaluation of the system resulted in eating activities detected with the maximum accuracy of 76.4% and an average accuracy of 71.9% using 10-folds cross-validation. CCS CONCEPTS• Ubiquitous and mobile computing → Activity prediction.

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Classification Method of Eating Behavior by Dietary Sound Collected in Natural Meal Environment

Cited by 2 publications

References 11 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

Prediction of Eating Activity using Smartwatch

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