The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation

By Feng Zhu, Marc Bosch, I. Woo, Sung-ye Kim, C. Boushey, David S. Ebert, Edward J. Delp III. IEEE Journal on Selected Topics in Signal Processing, 2010

381 citations recorded by the source corpus.

There is a growing concern about chronic diseases and other health problems related to diet including obesity and cancer. The need to accurately measure diet (what foods a person consumes) becomes imperative. Dietary intake provides valuable insights for mounting intervention programs for prevention of chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper, we describe a novel mobile telephone food record that will provide an accurate account of daily food and nutrient intake. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed. The mobile device provides a unique vehicle for collecting dietary information that reduces the burden on respondents that are obtained using more classical approaches for dietary assessment. We describe our approach to image analysis that includes the segmentation of food items, features used to identify foods, a method for automatic portion estimation, and our overall system architecture for collecting the food intake information.

Subfields: Biomedical Engineering, Nutrition and Dietetics, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/f0046eeca1d2b5096fa398934a8d53dc10f53cbc

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In Biomedical Engineering

1. Kevin P. Murphy
2. Kiyoharu Aizawa
3. George Papandreou
4. Yoshiyuki Kawano
5. Keiji Yanai

Citations in the corpus

• A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure (2018, background): “To address this issue, visual-based food recognition algorithms and systems have been proposed [6], [7], [8], [9], [10], [11].”
• A Survey on Food Computing (2018, background): “m PlateMate for crowdsourcing nutritional analysis from photographs of meals. These methods are semi-automatic and involve the participant of registered dietitians. To make the system fullautomatic, [Zhu et al. 2010,2008] proposed a technology assisted dietary assessment system, where images obtaine”
• Food Detection and Recognition Using Convolutional Neural Network (2014, methodology): “Regarding food image recognition, Zhu et al. [12] described food recognition using a small dataset, which was intended to be used in a smartphone-based food-logging sys-tem as part of their Technology Assisted Dietary Assessment project.”
• Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach (2017, neutral): “There is increasing usage of health apps among health care professionals, patients and general public [8], and apps can play a role in patient education, disease self-management, remote monitoring of patients, and collection of dietary data [9-12].”
• Im2Calories: Towards an Automated Mobile Vision Food Diary (2015, background): “The more challenging setting works outside of restaurants.”