Calorific expenditure estimation using deep convolutional network features

Baodong Wang, Lili Tao, Tilo Burghardt, Majid Mirmehdi

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings of the IEEE Winter Conference on Applications of Computer Vision 2018 (WACV18)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
DOIs
Publication statusE-pub ahead of print - 26 Apr 2018

Structured keywords

  • Digital Health

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  • Projects

    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.

    1/10/1330/09/18

    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