Difference between revisions of "Nourich"
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Nourich has an Android app version which is trained on food images and can recognize food labels from images. It uses one of the world's largest nutrition knowledge base, Edamame. Right now, the volume is input by the user and will be automatically estimated later(future work). The system architecture is below. | Nourich has an Android app version which is trained on food images and can recognize food labels from images. It uses one of the world's largest nutrition knowledge base, Edamame. Right now, the volume is input by the user and will be automatically estimated later(future work). The system architecture is below. | ||
| − | [[File:Nutrition app.png | Android app architecture | | + | [[File:Nutrition app.png | Android app architecture |700px|center]] |
===Chatbot=== | ===Chatbot=== | ||
Revision as of 00:04, 16 July 2019
Contents
Motivation and Background
Obesity, being one of the diseases with major comorbidities, is on the rise worldwide. In the United States, 36% of the adult population are obese [1]. Obesity can be caused by genetic and behavioral factors which include irregular eating patterns, lack of physical activity and the side-effect of medication usage [2]. According to The Dietary Guidelines of Americans 2010, focus on reducing excess calorie consumption and making an informed decision about food choices and physical activity can help attain a healthier weight and reduce the risk of chronic illness. While the weight trend can move towards a healthier range by controlled calorie consumption with essential nutrition intake, people/patients are unable to determine a healthy regimen for various reasons. Specifically, they are unaware of calorie content in the food they intake because it is not easy to determine it, and even if they do, keeping track of their food pattern and cumulative consumption is difficult.
NOURICH
Monitoring an individual's diet and cumulative calorie intake through food images and recommending meals can help them in making informed decisions about their meals. Also, tracking and assessing their food patterns and weight trends can help them maintain a healthier weight in the longer run. A system can be built that is trained to recognize food images collected from open sources such as Instagram, Google images, Pinterest, Getty image, etc. Once recognized, the volume can be estimated based on user input (automatically, in future) and nutrition information can be obtained using comprehensive knowledge bases. AI techniques support meal recommendations specific to user preferences and context.
Applications
Android App
Nourich has an Android app version which is trained on food images and can recognize food labels from images. It uses one of the world's largest nutrition knowledge base, Edamame. Right now, the volume is input by the user and will be automatically estimated later(future work). The system architecture is below.
