Food Intake Monitoring System for Mobile Devices Engin Mendi * , Ocal Ozyavuz † , Emrah Pekesen † and Coskun Bayrak *† * Computer Science Department, University of Arkansas at Little Rock, AR, USA {esmendi | cxbayrak}@ualr.edu † Computer Engineering Department, Istanbul Kultur University, Istanbul, Turkey {o.ozyavuz | e.pekesen | c.bayrak}@iku.edu.tr Abstract—In this paper, we introduce a real-time food intake monitoring system for mobile devices. The proposed system gets acceleration data from the sensor placed on the wrist of the user during a meal. The data is then sent to the mobile device via Bluetooth. The system analyses patterns between the motion profile and bite actions by first filtering the data to remove noise effects and then identifying the peaks. Based on detecting peaks, real-time feedback regarding eating trends such as total number of bites, bites-taken rate and eating speed is provided to the user. If the eating is too fast, the system warns the user in the form of both audio and text. The mobile application is implemented on the Android Platform and tested on a subject successfully. The proposed system offers an affordable quick solution that can be used in any place where eating happens. It can help people who are obese or with other eating disorders by monitoring consumption of food intake to control their eating rate in real- time. I. I NTRODUCTION Obesity is a medical condition in which excess body fat has accumulated to the extent that it may have an adverse effect on health, leading to reduced life expectancy and/or increased health problems [1]. Overweight is generally defined as having more body fat than is optimally healthy [2]. Obesity and overweight pose a major risk for serious diet-related chronic diseases, including type 2 diabetes, cardiovascular disease, hypertension and stroke, and certain forms of cancer. The health consequences range from increased risk of premature death, to serious chronic conditions that reduce the overall quality of life [3]. Obesity and overweight have been shown to increase the rate of several common adverse medical conditions, resulting in economic costs of $300 billion per year in the United States and Canada. These costs result from an increased need for medical care and the loss of economic productivity resulting from excess mortality and disability [4]. Results from the 2007-2008 National Health and Nutrition Examination Survey (NHANES) indicate that an estimated 34.2% of U.S. adults aged 20 years and over are overweight, 33.8% are obese, and 5.7% are extremely obese [5]. Globally, there are more than 1 billion overweight adults, at least 300 million of them obese. An estimated 17.6 million children under five are estimated to be overweight worldwide [6]. During past years, there has been a dramatic increase in obesity and overweight in the U.S. Between 1980 and 2000, obesity rates doubled among adults. About 60 million adults, or 30% of the adult population, are now obese. Similarly since 1980, overweight rates have doubled among children and tripled among adolescents - increasing the number of years they are exposed to the health risks of obesity [7]. In this paper, we present a food intake monitoring system for mobile devices. The system can monitor consumption of food intake during eating that can help to manage weight loss. Real-time feedback is provided to the user regarding eating trends such as bites-taken rate and eating speed. The proposed framework can help people with obesity, overweight or with other eating disorders to control their eating rate. II. SYSTEM DESCRIPTION An overview of the proposed system is given in Figure 1. First, the system gets acceleration data from the sensor that user wears on the wrist during a meal. Then, the acceleration data is sent to the mobile phone via Bluetooth. Gaussian smoothing filter is applied to the noisy data in order to reduce the effects of noise. By detecting peaks in smoothed acceleration data, the system identifies the bite actions in which one peak represents a bite taken. Real-time information is provided to the user regarding eating trends. The system counts the total number of bites the user has taken, and provides the bites-taken rate. Finally, user is provided about his eating speed whether normal or fast. If the user is eating too fast, the system warns him to slow down. Fig. 1: System overview. A. Bluetooth Communication The acceleration data of wrist motion of the user is transferred to the mobile phone via Bluetooth [8] wireless