Abstract
Healthy diet with balanced nutrition is key to the prevention of life-threatening diseases such as obesity, cardiovascular disease, and cancer. Recent advances in smartphone and wearable sensor technologies have led to a proliferation of food monitoring applications based on automated food image processing and eating episode detection, with the goal to conquer drawbacks of the traditional manual food journaling that is time consuming, inaccurate, underreporting, and low adherent. In order to provide users feedback with nutritional information accompanied by insightful dietary advice, various techniques in light of the key computational learning principles have been explored. This survey presents a variety of methodologies and resources on this topic, along with unsolved problems, and closes with a perspective and boarder implications of this field.
Highlights
Many people face challenges to maintain healthy diet and manage their weight these days, while knowing bad eating habits lead to overweight and obesity that increase the risk of heart diseases, hypertension, other metabolic comorbidities such as type 2 diabetes, and cancer [1]
Teenagers are willing to take food images using a mobile food recorder before eating [5]; and the dietary feedback contributes to weight loss [6]
We review the most relevant applications on automatic food monitoring that focus on addressing each aforementioned challenge
Summary
Vieira; Resende, Silva; and Cui, Juan, "A Survey on Automated Food Monitoring and Dietary Management Systems" (2017). This Article is brought to you for free and open access by the Computer Science and Engineering, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in CSE Journal Articles by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Published in final edited form as: J Health Med Inform.
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