# Smart Diet Diary: Real-Time Mobile Application for Food Recognition
## 1 Introduction  
Obesity is a worldwide issue that accounts for 8% of global deaths annually [1]. People with obesity are at higher risks for many serious diseases and health conditions, including high blood pressure, dyslipidemia, diabetes, cardiac arrest, gallbladder disease, osteoarthritis, sleeping problems, respiratory problems, mental illness, and an overall low quality of life. In addition, this creates problems in maintaining proper posture while working or sleeping, resulting in stress injuries [2, 3]. Although the genes of a person play a role in body weight [4], there are other reasons behind obesity, such as certain medications, stress, lack of exercise, and irregular sleep habits. Dietary intake is one of the major causes of excess weight and fat accumulation. A higher than required intake of food results in surplus calories that are not consumed. Researchers have explored dietary and activity assessments as solutions to raise awareness and address obesity. Recently, many healthcare studies have focused on dietary records and controls to provide useful information to prevent the occurrence of chronic illnesses. Currently, three types of methods are used to manually assess dietary intake: diet records [5], 24 h recall [6], and food frequency questionnaire (FFQ) [7]. For diet histories, users were required to store food intake over three consecutive days. The participants were provided with detailed instructions by trained staff on how to record intake, which was also stored in the Nutrition Data System for Research (NDSR) for analysis. The 24 h Recall (24 HR) is a structured interview that permits users to record comprehensive information of the diet consumed by the participant during the previous 24 h. It is primarily used by researchers to understand the habitual consumption of food. However, 24 HR has many drawbacks, including high cost and administration time [8]. The FFQ method requires the subject to report the frequency of food and beverage consumption over a period of up to one year. The majority of FFQs are accessible in paper or electronic formats, displayingcommon questions about daily diet. This requires participants to manually measure their food consumption and then record these facts in a notebook diary. There are several smartphone-based health applications equipped with features to keep record of the diet consumed and the physical activities performed; however, users must manually enter all their information. In addition, fine-grained and preemptive food intake measurement systems do not exist at present; they are either precise and consume too much time, or are less precise and unobtrusive. The dietary assessment systems that are not tedious and time-consuming are in high demand in the healthcare field. To target these two factors, researchers have placed an extensive amount of effort into analyzing dietary information analysis using visuals [3, 4, 5, 6, 7, 8]. The available solutions range from manual diet tracking (paper diaries or digital diary/smartphone apps) to more automated applications using image recognition. These solutions can be classified into three main categories: manual self-reporting methods, semi-automatic sensor methods, and automatic sensor and mobile methods. Recent developments in smartphone digital cameras have provided researchers with the opportunity to develop a system for accurate dietary assessment. Investigations have shown that the image-based approach is more accurate than the proxy-assisted method. Although several promising image-based calorie assessment systems exist, they emphasize on diet recognition and classification without considering food weight and volume. Therefore, the values obtained are not reliable, as without food mass, the calorie content of food cannot be precisely approximated. Thus, extracting the weight of food from an image is an important problem and needs attention. Manual methods rely on the self-reporting of food intake in paper diaries [5] or digital diaries in the form of a mobile app [9].