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Calorific expenditure estimation using deep convolutional network features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationProceedings of the IEEE Winter Conference on Applications of Computer Vision 2018 (WACV18)
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
DOIs
DateAccepted/In press - 30 Jan 2018
DateE-pub ahead of print (current) - 26 Apr 2018

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.

    Structured keywords

  • Digital Health

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at https://ieeexplore.ieee.org/document/8347114/ . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 1 MB, PDF-document

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