Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone
This work addresses energy expenditure estimation for health monitoring using smartphones, but it is incremental as it builds on existing activity recognition and energy expenditure translation methods.
The paper tackled the problem of estimating real-time energy expenditure non-intrusively by recognizing daily activities from smartphone sensor data, achieving 80% accuracy in recognizing 17 daily activities and estimating energy expenditure with a mean error of 26%.
This paper presents a 3-step system that estimates the real-time energy expenditure of an individual in a non-intrusive way. First, using the user's smart-phone's sensors, we build a Decision Tree model to recognize his physical activity (\textit{running}, \textit{standing}, ...). Then, we use the detected physical activity, the time and the user's speed to infer his daily activity (\textit{watching TV}, \textit{going to the bathroom}, ...) through the use of a reinforcement learning environment, the Partially Observable Markov Decision Process framework. Once the daily activities are recognized, we translate this information into energy expenditure using the compendium of physical activities. By successfully detecting 8 physical activities at 90\%, we reached an overall accuracy of 80\% in recognizing 17 different daily activities. This result leads us to estimate the energy expenditure of the user with a mean error of 26\% of the expected estimation.