Name
Abstract | ||
|---|---|---|
Accurate methods to measure food and energy intake are crucial for the battle against obesity. Providing users/patients with convenient and intelligent solutions that help them measure their food intake and collect dietary information are the most valuable insights toward long-term prevention and successful treatment programs. In this paper, we propose an assistive calorie measurement system to help patients and doctors succeed in their fight against diet-related health conditions. Our proposed system runs on smartphones, which allow the user to take a picture of the food and measure the amount of calorie intake automatically. In order to identify the food accurately in the system, we use deep convolutional neural networks to classify 10000 high-resolution food images for system training. Our results show that the accuracy of our method for food recognition of single food portions is 99%. The analysis and implementation of the proposed system are also described in this paper. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/I2MTC.2016.7520547 | 2016 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE PROCEEDINGS |
Keywords | Field | DocType |
calorie measurement, food recognition, segmentation, graph cut, deep neural network | Calorie intake,Convolutional neural network,Food recognition,Control engineering,Artificial intelligence,Food energy,Engineering,Deep learning,Calorie,Artificial neural network,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| parisa pouladzadeh | 1 | 100 | 8.14 |
| Pallavi Kuhad | 2 | 24 | 2.33 |
| Sri Vijay Bharat Peddi | 3 | 21 | 2.54 |
| abdulsalam yassine | 4 | 229 | 23.42 |
| Shervin Shirmohammadi | 5 | 1066 | 125.81 |