# Evaluation of the Chewing Pattern through an Electromyographic Device

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Development of a Device to Assess Chewing Behavior

#### 2.2. Sample Preparation and Characterization

- Number of chews (${n}_{chew}$ adimensional): the number of chews made by the subject (). The number of detected chewing cycles ${\u2206t}_{chew\_dx}$ is called ${n}_{chew\_dx}$. The whole process is repeated to calculate ${n}_{chew\_sx}$. The average of ${n}_{chew\_dx}$ and ${n}_{chew\_sx},$ gives an estimate of the number of chews ${n}_{chew}$:$${n}_{chew}=\frac{({n}_{chew\_dx}+{n}_{chew\_sx})}{2}$$
- Cycle Time (${t}_{cyc}$, second): the time spent on a single bite in seconds. ${t}_{cyc\_dx}$ is calculated as the ratio of the sum of all the time intervals of the chews ${\u2206t}_{chew\_dx}$ and the number of chews ${n}_{chew\_dx}$. The whole process is repeated to calculate ${t}_{cyc\_sx}$. The average of ${t}_{cyc\_dx}$ and ${t}_{cyc\_sx}$, gives an estimate of the cycle time ${t}_{cyc}$. This parameter is a good estimate of the chewing rate (in seconds). In the following, the full formula used to calculate ${t}_{cyc}$ is reported.$${t}_{cyc}=\frac{\left({t}_{cyc\_dx}+{t}_{cyc\_sx}\right)}{2}=\frac{\left(\frac{\sum {\u2206t}_{chew\_dx}}{{n}_{chew\_dx}}\right)+\left(\frac{\sum {\u2206t}_{chew\_sx}}{{n}_{chew\_sx}}\right)}{2}$$
- Chewing Time (${t}_{chew}$, second): the effective time in which the subject has chewed in seconds (), as expressed by the product between the number of chews and the cycle time, calculated according to the following equation:$${t}_{chew}=\frac{\left({t}_{chew\_dx}+{t}_{chew\_sx}\right)}{2}=\frac{\left({n}_{chew\_dx}\xb7{t}_{cyc\_dx}\right)+\left({n}_{chew\_sx}\xb7{t}_{cyc\_sx}\right)}{2}$$
- Work ($w$, volts * second): the estimated area under the masticatory signal. Right work ${w}_{dx}$ is the sum of the products between the mean voltage ($\overline{{v}_{dx}})$ and ${\u2206t}_{chew\_dx}.$ Dually, it is calculated as ${w}_{sx}$. The average between ${w}_{dx}$ and ${w}_{sx}$ is the work $w$.$$w=\frac{\left({w}_{dx}+{w}_{sx}\right)}{2}=\frac{\left(\sum \left(\overline{{v}_{dx}}\xb7{\u2206t}_{chew\_dx}\right)\right)+\left(\sum \left(\overline{{v}_{sx}}\xb7{\u2206t}_{chew\_sx}\right)\right)}{2}$$
- Work rate ($wr$, volt): indicates the power exerted by the masticatory muscles (in volts), which is expressed as the ratio between the work and chewing time. This feature is calculated as the ratio between the work and chewing time:$$wr=\frac{\left({wr}_{dx}+{wr}_{sx}\right)}{2}=\frac{\left((\frac{{w}_{dx}}{{t}_{chew\_dx}})\right)+\left((\frac{{w}_{sx}}{{t}_{chew\_sx}})\right)}{2}$$
- Asymmetry index ${i}_{as}$: is related to the number of chews of the masticatory, assessing whether it is balanced or not, and is calculated as follows:$${i}_{as}=mean\left({n}_{chew\_dx}\right)-mean\left({n}_{chew\_sx}\right)$$

- balanced if ${-1<i}_{as}<1$,
- slightly unbalanced to the right if ${0<i}_{as}<5$ or to left if ${-5<i}_{as}<0$
- unbalanced to the right ${i}_{as}>5$ or to the left if ${i}_{as}<-5$

#### 2.3. Statistics

## 3. Results

#### 3.1. Study Population

#### 3.2. Chewing Profiles of Smokers and Non-Smokers

#### 3.3. Graphical Clustering of the Chewing Profiles of Smokers and Non-Smokers

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Chewing device. (

**a**) Scheme of the device and all the parts that it includes: (5) Arduino nano 33 BLE microprocessor connected to a PC via cable (6), two Arduino muscle v3 modules (1) connected to the microprocessor through a resistive divider (4) and a 9 volt battery (3). The signal is taken through the electrodes (2) connected to the Arduino muscle v3 modules. (

**b**) Placement of EMG electrodes on both the masseters of a subject (1–2): the red ones on the central part of the muscles, the green ones at the end of the masseters and the yellow ones on the cheekbones.

**Figure 2.**Chewing registration of the signals ${v}_{dx}\left(t\right)$ and ${v}_{sx}\left(t\right)$. The signals shown in the figures have been rectified, amplified and filtered by the circuit modules, and any bias has been eliminated via software. (

**a**) Right masseter activity ${v}_{dx}\left(t\right)$. (

**b**) Left masseter activity ${v}_{sx}\left(t\right)$.

**Figure 3.**Characteristics of the group of people undergoing the test. The 25 subjects were divided into two macro-categories: smokers (9) and non-smokers (16). Each macro-category was divided into four sub-categories based on the age of the subjects (17–24, 25–40, 41–60, >60), of which the number of female (women) and male (men) subjects is indicated.

**Figure 4.**Examples of the chewing profiles of a smoker and a non-smoker. (

**a**) Chewing profile for the sample of bread of a smoker; (

**b**) Chewing profile for the sample of bread of a non-smoker. Chewing time and number of bites appear to be greater in the pattern of (

**b**).

**Figure 5.**Boxplots of the chewing features of smokers (in blue) and non-smokers (in red): in the first row of the figure, there are the boxplots of chewing time, number of chews and work features; in the second row, there are boxplots of cycle time, work rate and asymmetry index. The asterisks represent a significance level ≤ 0.05. The dot represents a significance level ≤ 0.1.

**Figure 6.**Representation of the data distribution in 2D space realized with ${n}_{chew}$ and w. The crosses in blue and red represent the geometric center of the distribution of smokers and non-smokers, respectively.

Food | Salt (100 g) | Fats (100 g) | Carbohydrates (100 g) | Proteins (100 g) | Sugars (100 g) | Fiber (100 g) |
---|---|---|---|---|---|---|

Bread | 0 | 6.5 | 58 | 10 | 4 | 2.5 |

Features | Mean Smokers | Standard Dev. Smokers | Mean Non-Smokers | Standard Dev. Non-Smokers | Statistical Test | p-Value ^{1} |
---|---|---|---|---|---|---|

Age | 43.88 | 14.4 | 46.79 | 20.73 | −0.33 | 0.32 |

Sex | 0.62 | 0.48 | 0.64 | 0.48 | 0.14 | 0.49 |

BMI | 25.64 | 3.02 | 24.38 | 2.02 | 1.11 | 0.16 |

${t}_{chew}$ | 3.15 | 1.66 | 4.39 | 1.39 | −1.78 | 0.07 ° |

${n}_{chew}$ | 6.31 | 2.6 | 9.82 | 3.13 | −2.56 | 0.02 * |

${t}_{cyc}$ | 0.5 | 0.25 | 0.57 | 0.38 | 53 | 0.34 |

$w$ | 0.06 | 0.03 | 0.11 | 0.04 | −3.12 | 0.01 * |

$wr$ | 0.02 | 0.01 | 0.03 | 0.01 | −0.99 | 0.17 |

${i}_{as}$ | 2 | 2.96 | −0.57 | 3.66 | 37 | 0.14 |

^{1}t-test and Mann–Whitney test based on Shapiro’s test for data normality. Statistically significant differences are reported in bold. (*) stands for p-value < 0.05; (°) stands for p-value $\le 0.1$

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**MDPI and ACS Style**

Riente, A.; Abeltino, A.; Serantoni, C.; Bianchetti, G.; De Spirito, M.; Capezzone, S.; Esposito, R.; Maulucci, G.
Evaluation of the Chewing Pattern through an Electromyographic Device. *Biosensors* **2023**, *13*, 749.
https://doi.org/10.3390/bios13070749

**AMA Style**

Riente A, Abeltino A, Serantoni C, Bianchetti G, De Spirito M, Capezzone S, Esposito R, Maulucci G.
Evaluation of the Chewing Pattern through an Electromyographic Device. *Biosensors*. 2023; 13(7):749.
https://doi.org/10.3390/bios13070749

**Chicago/Turabian Style**

Riente, Alessia, Alessio Abeltino, Cassandra Serantoni, Giada Bianchetti, Marco De Spirito, Stefano Capezzone, Rosita Esposito, and Giuseppe Maulucci.
2023. "Evaluation of the Chewing Pattern through an Electromyographic Device" *Biosensors* 13, no. 7: 749.
https://doi.org/10.3390/bios13070749