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Publication - Dr Tilo Burghardt

    Calorific expenditure estimation using deep convolutional network features

    Citation

    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)

    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.

    Full details in the University publications repository