## 1 Introduction  
Healthy eating and calorie balancing are key factors for living a healthy life with a reduced risk of chronic diseases such as Knee Osteoarthritis (OA), cancers, heart disease, cirrhosis, and diabetes [1]. In OA, the pressure coming from the increased weight causes degradation of joint cartilage and the underlying bone. This, OA consequently causes stiffness and severe pain to the patient. There have been attempts to design an automated food monitoring system that measures caloric intake by classifying types of food and estimating ingested amounts. Different sensors such as piezoelectric sensors, microphone, accelerometer, gyroscope, camera, weight scale, electromyograph, and textile pressure sensor have been employed to automatically monitor food intake. Among these sensors, microphones have been widely used because they are more accurate than other sensors [2, 3, 4, 5, 6, 7, 8, 9]. Microphone-based wearables collect acoustic signals non-invasively while the wearer is eating; however, surrounding noise affects the performance and decreases the accuracy of food classification in real environments. Furthermore, these wearables have the disadvantages of low user acceptance for long-term usage [4] and low comfortability because they exert a large force against the neck [2; 3]. On the other hand, the necklace-embedded piezoelectric sensor, also known as vibration sensor, generates distinct patterns for the chewing sequence and swallowing activity by detecting skin motion of the neck and jaw. The employed sensors are not only less sensitive to surrounding noise but also provide better user comfortability than microphone-based wearables. Nowadays, the severity of chronic diseases has increased such that they become difficult to cure with traditional medicine. Medicine may cause several severe side effects while curing a disease. Thus, several other diseases attack the human body when using medicine for cure. Chronic diseases can be prevented if timely preventive steps are taken. Automated wearable monitoring systems are essential for assisting users in following preventive measures. Thus, it motivated us to design a novel food-intake monitoring system that can enable users to control their food intake and avoid deadly chronic diseases. In this work, we present a smartphone application-based nutrition monitoring wearable system that classifies food categories and estimates caloric intake by taking advantage of the robust features of a piezoelectric embedded necklace. Our system classifies foods into different categories based on chewing and swallowing patterns and estimates the volume and calories of food ingested based on the number of swallows. As shown in Figure 1, our smartphone application sends statistics about caloric intake, suggests the next mealtime, and recommends physical exercise. The users can develop good healthy dietary behavior by following the recommended suggestions. There are three components of our proposed food intake system. parts. First, we have achieved a higher classification accuracy than previous nutrition monitoring systems [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. We have demonstrated that accurately processed raw temporal data along with an important set of optimal feature selection, enables the proposed system to outperform other state-of-the-art necklace systems [10; 11] that use spectral features to recognize foods. We have observed that a temporal frame of 30 samples (1.5 s) containing a sequence of chewing events combined with a successive swallowing event forms a distinct pattern. The distinct temporal patterns in the frames generate essential effective features to associate the pattern with a relevant food category. Our proposed system has attained f-measures of 94.2%, 93.7%, and 95.1% using the k-nearest neighbor, support vector machine, and random forest, respectively.