Automatic Detection of Chewing and Swallowing

Nakamura, Akitada; Saito, Takato; Ikeda, Daizo; Ohta, K.; Mineno, Hiroshi; Nishimura, Masafumi

doi:10.3390/s21103378

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…Early studies mainly focused on the gnathosonic which is related to occlusal stability and interference [ 13 – 16 ]. To date, the related studies have mainly been based on the classification in occlusal sounds of Watt’s work [ 44 , 45 ]. Due to limitations of adapterization and analysis methods, the subsequent doubt centralized the methodology, which lacked meaningful data analysis and complete sound capture [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

Study of occlusal acoustic parameters in assessing masticatory performance

Xia

Wang

2022

BMC Oral Health

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Background Previous masticatory studies have focused on a variety of measurements of foods and boluses or kinematic parameters and sound during mastication. To date, the masticatory sound research of has been limited due to the difficulties of sound collection and accurate analysis. Therefore, significant progress in masticatory sound has not been made. Meanwhile, the correlation between acoustic parameters and mastication performance remains unclear. For the purpose of exploring the acoustic parameters in measuring mastication performance, the bone-conduction techniques and sound analysis were used, and a statistical analysis of acoustic and occlusal parameters were conducted. Methods The gnathosonic and chewing sounds of fifty-six volunteers with healthy dentate were recorded by a bone-conduction microphone and further analyzed by Praat 5.4.04 when intercuspally occluding natural foods (peanuts) were consumed. The granulometry of the expectorated boluses from the peanuts was characterized by the median particle size of the whole chewing sequence (D50a) and the median particle size during the fixed chewing strokes (D50b). The chewing time of the whole chewing sequence (CTa), the chewing time of the fixed chewing strokes (CTb), the chewing cycles (CC), and the chewing frequency (CF) were recorded and analyzed by the acoustic software. The acoustic parameters, including gnathosonic pitch, gnathosonic intensity, mastication sound pitch of the whole chewing sequence (MPa), mastication sound pitch of the fixed chewing strokes (MPb), mastication sound intensity of the whole chewing sequence (MIa) and mastication sound intensity of the fixed chewing strokes (MIb), were analyzed. Independent sample t-test, Spearman and Pearson correlation analyses were used where applicable. Results Significant difference in parameters CC, MIa, CF and D50a were found by sex (t-test, p < 0.01). The masticatory degree of the test foods was higher in women (CC, 24.25 ± 5.23; CF, 1.70 ± 0.21 s−1; D50a, 1655.07 ± 346.21 μm) than in men (CC, 18.14 ± 6.38; CF, 1.48 ± 0.18 s−1; D50a, 2159.21 ± 441.26 μm). In the whole chewing sequence study, a highly negative correlation was found between MIa and D50a, and a highly positive correlation was found between MIa and CF (r = − 0.94, r = 0.82, respectively, p < 0.01). No significant correlation was found between the remaining acoustic parameters and mastication parameters. In the fixed chewing strokes study, a highly negative correlation was found between MIb and D50b (r = − 0.85, p < 0.01). There was no significant correlation between the rest of the acoustic parameters and the mastication parameters. Conclusions Mastication sound intensity may be a valuable indicator for assessing mastication. Acoustic analysis can provide a more convenient and quick method of assessing mastication performance.

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Section: Discussionmentioning

confidence: 99%

Study of occlusal acoustic parameters in assessing masticatory performance

Xia

Wang

2022

BMC Oral Health

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“…During experimentation, it had been found that the Tiny Eats GRU only used 4% of the Arm Cortex M0 + memory and had a lag of 6 ms with a 95.15% accuracy rate when figuring out if an individual was eating or not. Nakamura et al (2021) 17 worked on making an automated way to recognize different items of food based on the sounds made while eating. A combined CTC/attention model was used by the researchers to automatically find left chewing, front biting, right chewing, and swallowing.…”

Section: Related Workmentioning

confidence: 99%

Automated detection and recognition system for chewable food items using advanced deep learning models

Kumar,

Koul,

Kamini

et al. 2024

Sci Rep

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Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.

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“…In recent years, studies have focused on non-invasive and simple methods for evaluating swallowing function, such as evaluation of the tongue, hyoid bone movement, and tongue pressure using ultrasonography, electromyography, and pressure sensors, as well as the assessment of mastication and swallowing using microphones [ 11 , 12 , 13 , 14 ]. All these methods are non-invasive and useful means for obtaining information, but we cannot rule out the possibility that the method of applying an ultrasonic probe to the mandible while swallowing or attaching a sensor to the skin near the oral cavity or larynx may interfere with the movement of various organs involved in swallowing or may cause discomfort.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, since examination circumstances that vary from routine environments can be the cause of confusion or stress for patients with dementia, it would be ideal for the evaluation of swallowing function to be performed in daily eating/dietary environments [ 16 , 17 ]. Although there have been many studies exploring simple methods to evaluate tongue movement, tongue pressure, and hyoid bone movement [ 11 , 12 , 13 , 14 ], a method that can easily evaluate the soft palate movement, despite its important role in preventing regurgitation of food bolus in the nasal canal while swallowing and generating pressure for the transport of the food bolus, remains to be established [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of Earphone-Type Sensors for Non-Invasive and Objective Swallowing Function Assessment

Yoshimoto

Taniguchi

Kurose

et al. 2022

Sensors

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Standard methods for swallowing function evaluation are videofluoroscopy (VF) and videoendoscopy, which are invasive and have test limitations. We examined the use of an earphone-type sensor to noninvasively evaluate soft palate movement in comparison with VF. Six healthy adults wore earphone sensors and swallowed barium water while being filmed by VF. A light-emitting diode at the sensor tip irradiated infrared light into the ear canal, and a phototransistor received the reflected light to detect changes in ear canal movement, including that of the eardrum. Considering that the soft palate movement corresponded to the sensor waveform, a Bland–Altman analysis was performed on the difference in time recorded by each measurement method. The average difference between the time taken from the most downward retracted position before swallowing to the most upward position during swallowing of the soft palate in VF was −0.01 ± 0.14 s. The Bland–Altman analysis showed no fixed or proportional error. The minimal detectable change was 0.28 s. This is the first noninvasive swallowing function evaluation through the ear canal. The earphone-type sensor enabled us to measure the time from the most retracted to the most raised soft palate position during swallowing and validated this method for clinical application.

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Automatic Detection of Chewing and Swallowing

Cited by 14 publications

References 16 publications

Study of occlusal acoustic parameters in assessing masticatory performance

Study of occlusal acoustic parameters in assessing masticatory performance

Automated detection and recognition system for chewable food items using advanced deep learning models

Validation of Earphone-Type Sensors for Non-Invasive and Objective Swallowing Function Assessment

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