Evaluation of the Chewing Pattern through an Electromyographic Device

Abstract

Chewing is essential in regulating metabolism and initiating digestion. Various methods have been used to examine chewing, including analyzing chewing sounds and using piezoelectric sensors to detect muscle contractions. However, these methods struggle to distinguish chewing from other movements. Electromyography (EMG) has proven to be an accurate solution, although it requires sensors attached to the skin. Existing EMG devices focus on detecting the act of chewing or classifying foods and do not provide self-awareness of chewing habits. We developed a non-invasive device that evaluates a personalized chewing style by analyzing various aspects, like chewing time, cycle time, work rate, number of chews and work. It was tested in a case study comparing the chewing pattern of smokers and non-smokers, as smoking can alter chewing habits. Previous studies have shown that smokers exhibit reduced chewing speed, but other aspects of chewing were overlooked. The goal of this study is to present the device and provide additional insights into the effects of smoking on chewing patterns by considering multiple chewing features. Statistical analysis revealed significant differences, as non-smokers had more chews and higher work values, indicating more efficient chewing. The device provides valuable insights into personalized chewing profiles and could modify unhealthy chewing habits.

Keywords: EMG device; chewing features; chewing profile; mastication; smoking; statistical analysis.

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.