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Article

Automatic Detection of Chewing and Swallowing †

1
Graduate School of Integrated Science and Technology, Shizuoka University, Hamamatsu 432-8011, Japan
2
NTT DOCOMO, Inc., Tokyo 100-6150, Japan
*
Author to whom correspondence should be addressed.
This Manuscript Is Extension Version of the Conference Paper: Nakamura, A.; Saito, T.; Ikeda, D.; Ohta, K.; Mineno, H.; Nishimura, M. Automatic Detection of Chewing and Swallowing Using Hybrid CTC/Attention. IEEE 9th Global Conference on Consumer Electronics, Kobe, Japan, 13–16 October 2020. Nakamura, A.; Saito, T.; Ikeda, D.; Ohta, K.; Mineno, H.; Nishimura, M. A Data Augmentation Technique for Automatic Detection of Chewing Side and Swallowing. APSIPA 2020, Auckland, New Zealand, 7–10 December 2020.
Academic Editor: Hong Kook Kim
Sensors 2021, 21(10), 3378; https://doi.org/10.3390/s21103378
Received: 31 March 2021 / Revised: 3 May 2021 / Accepted: 8 May 2021 / Published: 12 May 2021
(This article belongs to the Special Issue Acoustic Event Detection and Sensing)
A series of eating behaviors, including chewing and swallowing, is considered to be crucial to the maintenance of good health. However, most such behaviors occur within the human body, and highly invasive methods such as X-rays and fiberscopes must be utilized to collect accurate behavioral data. A simpler method of measurement is needed in healthcare and medical fields; hence, the present study concerns the development of a method to automatically recognize a series of eating behaviors from the sounds produced during eating. The automatic detection of left chewing, right chewing, front biting, and swallowing was tested through the deployment of the hybrid CTC/attention model, which uses sound recorded through 2ch microphones under the ear and weak labeled data as training data to detect the balance of chewing and swallowing. N-gram based data augmentation was first performed using weak labeled data to generate many weak labeled eating sounds to augment the training data. The detection performance was improved through the use of the hybrid CTC/attention model, which can learn the context. In addition, the study confirmed a similar detection performance for open and closed foods. View Full-Text
Keywords: chewing; swallowing; eating behavior; hybrid CTC/attention model; data augmentation chewing; swallowing; eating behavior; hybrid CTC/attention model; data augmentation
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MDPI and ACS Style

Nakamura, A.; Saito, T.; Ikeda, D.; Ohta, K.; Mineno, H.; Nishimura, M. Automatic Detection of Chewing and Swallowing. Sensors 2021, 21, 3378. https://doi.org/10.3390/s21103378

AMA Style

Nakamura A, Saito T, Ikeda D, Ohta K, Mineno H, Nishimura M. Automatic Detection of Chewing and Swallowing. Sensors. 2021; 21(10):3378. https://doi.org/10.3390/s21103378

Chicago/Turabian Style

Nakamura, Akihiro, Takato Saito, Daizo Ikeda, Ken Ohta, Hiroshi Mineno, and Masafumi Nishimura. 2021. "Automatic Detection of Chewing and Swallowing" Sensors 21, no. 10: 3378. https://doi.org/10.3390/s21103378

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