# Smart Diet Diary: Real-Time Mobile Application for Food Recognition
## 2 Related Work  
Mobile technology is developing swiftly, and each generation is more capable and computationally powerful than the preceding one. Mobile multimedia services and applications, along with the quick development of wireless internet technologies that offer high data rates and wide-scale device connectivity, have the potential to completely change the healthcare industry. Numerous studies have been carried out to examine how mobile apps affect medical procedures. [25, 26]. Likewise, social media usage addressing health-related issues has also been studied [27, 28]. A comprehensive review and rating of mobile applications is presented in [29]. There are several approaches for food recognition. These are primarily based on sensors and images, are data-driven and hybrid, combining multiple approaches. The sensor-based approaches for food recognition use electronic sensors to detect the physical and chemical properties of food and then identify the food based on those properties. Most commonly used sensors include piezoelectric sensor [30, 31, 32], microphones [33], textile pressure sensor [34], ultrasonic sensor [35], accelerometers [36], and a combination of accelerometer, gyroscope, and piezoelectric [37]. Electronic sensor-based systems rely on machine learning algorithms for food recognition. These approaches can only recognize a limited number of food items with low accuracy. Image-based recognition approaches use computer vision techniques to analyze images of food and identify their contents, detecting features sch as color, texture, and shape [38]. [PERSON] et al. [12] recognized food categories using eButton, a camera in a button worn at the chest, for detecting common eating utensils and food. The food image was segmented using color and texture features, whereas the volume was anticipated through the wire-mesh technique. DietCam [20] required three images from different sides of the dish to construct a 3D shape of the food. The regular-shaped food is approximated as cylinders or spheres. From this, the volume is guessed, and calories are computed from the calorie density stored in a database. The accuracy of food detection is further enhanced by matching the dish with the menu of nearby restaurants based on the GPS location obtained from the phone. The author later employed a multi-view recognition approach to identify each food item by determining the optimal viewpoint and employed a probabilistic technique for recognition [39]. Menu-Match [40] identifies the food items and estimates calories from a single food image by utilizing a database of known food items, i.e., a menu. It uses a single image of the meal while factoring in its geometric properties with the help of shape templates to reconstruct the 3D map of the food and calculate its volume estimation. The author used GPS to restrict searches to restaurants within a small vicinity. NutriTrack [15] informs the users regarding the nutritional value of their food and their required calorie intake by using traditional image processing [41]. [PERSON] et al. [42] annotated a large number of food items with their nutritional values and stored them in a database. An image of the food was taken and then used to search similar food items to provide the calorie count, but this method is not accurate. Ref. [43] used Kinect cameras, which provides color as well depth information to estimate the calorie count of food, but they are impractical as mobiles are not equipped with depth cameras. Data-driven recognition-based approaches use machine learning algorithms to analyze big datasets of the images of food and their labels. The algorithms learn to recognize patterns in the data and can be used to categorize new images using their similarity to the existing dataset [44]. There are several machine learning algorithms that are commonly used for food recognition such as SVM, random forests (RF), CNNs, deep belief networks (DBNs), and RNNs.