Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone

TL;DR

This paper introduces a non-intrusive system using smartphone sensors to recognize physical and daily activities, enabling real-time energy expenditure estimation with reasonable accuracy.

Contribution

The study develops a three-step system combining activity recognition and reinforcement learning to estimate energy expenditure from smartphone data.

Findings

Achieved 90% accuracy in recognizing 8 physical activities

Recognized 17 daily activities with 80% accuracy

Estimated energy expenditure with a mean error of 26%

Abstract

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.