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)

2 Citations (Scopus)
223 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
  • SPHERE

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