Evaluation of Different Radar Placements for Food Intake Monitoring Using Deep Learning

Author:

Wang Chunzhuo1,Kumar T. Sunil1,De Raedt Walter2,Camps Guido3,Hallez Hans4,Vanrumste Bart1

Affiliation:

1. KU Leuven,The e-Media Research Lab, and the ESAT-STADIUS Division,Leuven,Belgium

2. IMEC,The Life Science Department,Heverlee,Belgium

3. Wageningen University and Research, and the OnePlanet Research Center,The Division of Human Nutrition and Health,Department of Agrotechnology and Food Sciences,Wageningen,Netherlands

4. KU Leuven,The M-Group, DistriNet,Department of Computer Science,Sint-Michiels,Belgium

Funder

KU Leuven

Publisher

IEEE

Reference17 articles.

1. Annotation by category - ELAN and ISO DCR;sloetjes;Proc 6th Int Conf Lang Resour Eval Lr,0

2. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling;bai,2018

3. Temporal Convolutional Networks for Action Segmentation and Detection

4. AI-Assisted Food Intake Activity Recognition Using 3D mmWave Radars

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