@inproceedings{7ae7c1dbba604c2b876a65fd7aea33f6,
title = "FIT-Eve and ADAM: Estimation of velocity and energy for automated diet activity monitoring",
abstract = "State-of-the-art techniques for eating activities analysis in dietary monitoring require significant user intervention, which is reported to be one of the major reasons for low adherence. There are limited works using wearables for fine-grained analysis of eating activities in terms of the eating speed, the type of food consumed, and the portion sizes. In this paper, we propose FIT-EVE&ADAM, an armband based diet monitoring system that provides such fine-grained analysis, triggered by a single hand gesture. The system collects the user's gesture using sensors such as electromyogram embedded in the armband device, along with food image data using color and thermal cameras. Finally, a novel feature selection method is applied on the data features to estimate eating speed and caloric intake with high accuracy (0.96 F1 score).",
keywords = "Diet Monitoring, Gesture Recognition, Wearable",
author = "Junghyo Lee and Prajwal Paudyal and Ayan Banerjee and Sandeep Gupta",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 ; Conference date: 18-12-2017 Through 21-12-2017",
year = "2017",
doi = "10.1109/ICMLA.2017.000-7",
language = "English (US)",
series = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1071--1074",
editor = "Xuewen Chen and Bo Luo and Feng Luo and Vasile Palade and Wani, {M. Arif}",
booktitle = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
}