Calorific Expenditure Estimation Using Deep Convolutional Network Features
Abstract
Accurately estimating a person's energy expenditure is an important tool in tracking physical activity levels for healthcare and sports monitoring tasks, amongst other applications. In this paper, we propose a method for deriving calorific expenditure based on deep convolutional neural network features (within a healthcare scenario). Our evaluation shows that the proposed approach gives high accuracy in activity recognition (82.3%) and low normalised root mean square error in calorific expenditure prediction (0.41). It is compared against the current state-ofthe-art calorific expenditure estimation method, based on a classical approach, and exhibits an improvement of 7.8% in the calorific expenditure prediction task. The proposed method is suitable for home monitoring in a controlled environment.
Cite
Text
Wang et al. "Calorific Expenditure Estimation Using Deep Convolutional Network Features." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2018. doi:10.1109/WACVW.2018.00014Markdown
[Wang et al. "Calorific Expenditure Estimation Using Deep Convolutional Network Features." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2018.](https://mlanthology.org/wacvw/2018/wang2018wacvw-calorific/) doi:10.1109/WACVW.2018.00014BibTeX
@inproceedings{wang2018wacvw-calorific,
title = {{Calorific Expenditure Estimation Using Deep Convolutional Network Features}},
author = {Wang, Baodong and Tao, Lili and Burghardt, Tilo and Mirmehdi, Majid},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision Workshops},
year = {2018},
pages = {69-76},
doi = {10.1109/WACVW.2018.00014},
url = {https://mlanthology.org/wacvw/2018/wang2018wacvw-calorific/}
}