Calorific expenditure estimation using deep convolutional network features

Baodong Wang; Lili Tao; Tilo Burghardt; Majid Mirmehdi

doi:10.1109/WACVW.2018.00014

Calorific expenditure estimation using deep convolutional network features

Baodong Wang, Lili Tao, Tilo Burghardt, Majid Mirmehdi

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

1 Citation (Scopus)

218 Downloads (Pure)

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-of-the-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.

Original language	English
Title of host publication	Proceedings of the IEEE Winter Conference on Applications of Computer Vision 2018 (WACV18)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
DOIs	https://doi.org/10.1109/WACVW.2018.00014
Publication status	E-pub ahead of print - 26 Apr 2018

Structured keywords

Digital Health

Access to Document

10.1109/WACVW.2018.00014

Full-text PDF (accepted author manuscript)
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Accepted author manuscript, 1.86 MB

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Projects

1 Finished

SPHERE (EPSRC IRC)

Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Aldamen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.

1/10/13 → 30/09/18

Project: Research, Parent

Cite this

Wang, B., Tao, L., Burghardt, T., & Mirmehdi, M. (2018). Calorific expenditure estimation using deep convolutional network features. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision 2018 (WACV18) Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/WACVW.2018.00014

@inproceedings{79c8cef74c964b738a5e854f0721f5ad,

title = "Calorific expenditure estimation using deep convolutional network features",

abstract = "Accurately estimating a person{\textquoteright}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 derivingcalorific 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-of-the-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.",

author = "Baodong Wang and Lili Tao and Tilo Burghardt and Majid Mirmehdi",

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Calorific expenditure estimation using deep convolutional network features. / Wang, Baodong; Tao, Lili; Burghardt, Tilo ; Mirmehdi, Majid.

Proceedings of the IEEE Winter Conference on Applications of Computer Vision 2018 (WACV18). Institute of Electrical and Electronics Engineers (IEEE), 2018.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AU - Mirmehdi, Majid

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AB - 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 derivingcalorific 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-of-the-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.

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