## 2 Literature Review  
There has been significant effort by various researchers in the wearable healthcare field to design a wearable sensor to monitor and measure dietary intake. Researchers have designed various automated non-invasive food intake recognition systems using different sensors such as microphones [2, 4, 5, 6, 7, 8, 9, 17], accelerometers [14], cameras [18], gyroscopes [21, 22, 23, 24], textile pressure sensors [27], strain gauges [28], piezoelectric sensors [3, 10, 11, 13, 19], and orientation sensors [15, 29]. Recently, to estimate food quantity for the purpose of health monitoring, smartwatches [4], bite counters [21, 22, 23, 24], video recordings [18], and smart dining tables [26, 27] have been used. Smartwatch-based eating-behavior evaluation [4] has been proposed as a new modality for food intake monitoring. This system uses the microphone of the smartwatch, and has been used by many researchers in different configurations [2, 4, 5, 6, 7, 8, 9, 17]. The smartwatch-based system has limitations such as the effects of surrounding environmental noise and the inability to estimate the quantity of food. In addition to microphone-based systems, other sensor-based systems have many limitations compared to piezoelectric sensor-based systems, such as their fixed environmental settings [26, 27] or the limited scope of bite counters [21, 22, 23, 24]. We have used a necklace embedded with a piezoelectric sensor to track or transform the movement of neck skin into a suitable voltage. As each class of food is different to another, so bites of different foods cause different amounts of movement in the neck skin. Hence, the piezoelectric-sensor embedded necklace was chosen to accurately represent different skin movements with distinct patterns of voltages. The dietary intake recognition systems based on piezoelectric sensors are summarized in Table 1. [PERSON] et al. developed a wearable sensor system that consisted of a necklace to monitor nutrition intake [10, 11]. The necklace was embedded with a piezoelectric sensor to detect skin motion in the lower trachea during ingestion. The authors' method of food classification was applied on a limited number of binary categories such as liquid and solid, cold and hot, and soft and hard. They developed a classification model using statistical features extracted from a spectrogram. The authors used a long sample window and an improper swallowing position that obscured the swallowing characteristics. Accordingly, their spectrogram did not exhibit salient statistical features over time. Thus, this model performed only moderately at the cost of high complexity and extra computation of the spectrogram. [PERSON] et al. investigated the concept of food intake monitoring using a low-cost piezoelectric-sensor embedded in a necklace [13, 19]. The sensor generated a signal with a distinct pattern that enabled the system to recognize water, sandwic, and potato chips. They reported food classification accuracies of 81.4%, 84.5%, and 85.3% for water, sandwic, and chips, respectively. The authors' methodology obtained a better performance than [10, 11] by processing the temporal data and avoiding the computationally complex short-time Fourier transform (STFT). [PERSON] et al. compared a piezoelectric sensor and a microphone, the two most promising intake measuring sensors for automated dietary intake monitoring systems [3]. They obtained better food classification with the microphone than with the piezoelectric sensor. They applied a long sample window that failed to accurately process the data and chose an unsuitable position for the swallowing event. The authors obtained features from a spectrogram as they were inspired by [10,11]. The spectrogram-based signal-processing technique was better than matching pursuit and scalogram-based Gabor wavelets. However, as mentioned earlier, efficient features can be obtained from the temporal signal without the use of a spectrogram if a proper frame length and a suitable swallowing position are selected.