Calorific Expenditure Estimation Using Deep Convolutional Network Features
Baodong Wang, Lili Tao, Tilo Burghardt, Majid Mirmehdi · 2018
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-ofthe-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.