Effect of food properties and chewing condition on the electromyographic activity of the posterior tongue

Manda, Yosuke; Kodama, Naoki; Maeda, Naoto; Minagi, Shogo

doi:10.1111/joor.12774

Cited by 13 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this study, the EMG was measured by chewing gum. In actual use, since EMG can appear differently depending on the food eaten [ 39 ], input parameters for thresholding may be required. The user may urgently need to stop the system, and EEG may be exposed to various artifacts.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Brain–Computer Interface for Real-Life Meal-Assist Robot Control

Park

et al. 2021

Sensors

View full text Add to dashboard Cite

Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring additional time and effort. Thus, we developed a hybrid brain–computer interface-based meal-assist robot system following three features that can be measured using scalp electrodes for electroencephalography. The following three procedures comprise a single meal cycle. (1) Triple eye-blinks (EBs) from the prefrontal channel were treated as activation for initiating the cycle. (2) Steady-state visual evoked potentials (SSVEPs) from occipital channels were used to select the food per the user’s intention. (3) Electromyograms (EMGs) were recorded from temporal channels as the users chewed the food to mark the end of a cycle and indicate readiness for starting the following meal. The accuracy, information transfer rate, and false positive rate during experiments on five subjects were as follows: accuracy (EBs/SSVEPs/EMGs) (%): (94.67/83.33/97.33); FPR (EBs/EMGs) (times/min): (0.11/0.08); ITR (SSVEPs) (bit/min): 20.41. These results revealed the feasibility of this assistive system. The proposed system allows users to eat on their own more naturally. Furthermore, it can increase the self-esteem of disabled and elderly peeople and enhance their quality of life.

show abstract

Section: Discussionmentioning

confidence: 99%

A Hybrid Brain–Computer Interface for Real-Life Meal-Assist Robot Control

Park

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, we did not evaluate the effect of the number of chewing cycle on food particles. Manda et al showed that food properties had an effect on the number of chewing cycles, which indicated that the number of chewing cycles may influence on food particle properties. As subjects were instructed to masticate freely in this study, the number of chewing cycles was not evaluated.…”

Section: Discussionmentioning

confidence: 99%

Effects of stage II transport and food bolus sampling methods on the properties of food particles

Tanaka

Sugimoto

Kodama

et al. 2019

J of Oral Rehabilitation

Self Cite

View full text Add to dashboard Cite

During mastication, some portion of the food bolus is gradually transported to the oropharynx before deglutition, which is known as stage II transport (STII). Although the importance of STII in mastication and deglutition has been widely acknowledged, food particle properties that are transported into the oropharynx by STII have not yet been fully specified. To reveal the food particle properties that are transported into the oropharynx by STII and to assess the usefulness of new food bolus sampling methods for the evaluation of masticatory efficiency. Twenty‐two healthy volunteers participated in this study. Four different bolus sampling conditions were adopted: (a) the whole food particles were expectorated when the subject was aware of the first deglutition, (b) the last bolus to be swallowed at the end of unrestrained food intake was expectorated (aftermost bolus sample), (3) the whole food particles were expectorated when the subject felt ready to swallow after swallow‐inhibited mastication (swallow‐inhibited sample), (4) the particles were regurgitated from the oropharynx after the first STII (stage II‐transported sample). Food particles were analysed using the homogeneity index and particle size index. There was no significant difference between food particles in the aftermost bolus sample and swallow‐inhibited sample. The particles in the stage II‐transported sample showed significantly more homogeneous and smaller sizes than other sampling conditions (P < .05). The food particles transferred to the oropharynx in the stage II‐transported sample were smaller than those broken down by natural mastication.

show abstract

“…Sato analyzed the effect of taste on sEMG during consumption [32] and Miura analyzed the effect of taste, carbonation, and temperature on sEMG during swallowing [33]. Manda analyzed the effect of food properties and chewing conditions on sEMG during chewing [34]. However, the process of swallowing or chewing is different from stable stimuli of taste without voluntary muscle movements.…”

Section: Introductionmentioning

confidence: 99%

Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography

Wang

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine (SVM). The feasibility of quantitatively recognizing the intensity of Sour, Bitter, and Salty was verified. The sEMG signals were acquired under the stimuli of citric acid (aq), sucrose (aq), magnesium chloride (aq), sodium chloride (aq), and sodium glutamate (aq) with different concentrations, for five types of primary tastes: Sour, Sweet, Bitter, Salty, and Umami, whose order was fixed in this article. The acquired signals were processed with a method called Quadratic Variation Reduction to remove baseline wandering, and an adaptive notch to remove power frequency interference. After extracting 330 features for each sample, an SVM regressor with five-fold cross-validation was performed and the model reached R2 scores of 0.7277, 0.1963, 0.7450, 0.7642, and 0.5055 for five types of primary tastes, respectively, which manifested the feasibilities of the quantitative recognitions of Sour, Bitter, and Salty. To explore the facial responses to taste stimuli, we summarized and compared the muscle activities under stimuli of different taste types and taste intensities. To further simplify the model, we explored the impact of feature dimensionalities and optimized the feature combination for each taste in a channel-wise manner, and the feature dimensionality was reduced from 330 to 210, 120, 210, 260, 170 for five types of primary tastes, respectively. Lastly, we analyzed the model performance on multiple subjects and the relation between the model’s performance and the number of experiment subjects. This study can provide references for further research and applications on taste stimuli recognition with sEMG.

show abstract

Effect of food properties and chewing condition on the electromyographic activity of the posterior tongue

Cited by 13 publications

References 21 publications

A Hybrid Brain–Computer Interface for Real-Life Meal-Assist Robot Control

A Hybrid Brain–Computer Interface for Real-Life Meal-Assist Robot Control

Effects of stage II transport and food bolus sampling methods on the properties of food particles

Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography

Contact Info

Product

Resources

About