Real-time estimation of physical activity intensity for daily living

Estimating a person's energy expenditure and activity intensity over time is an important component in managing various health conditions or tracking lifestyle choices. To implement an automatic estimation, most current systems ultimately require users to wear sensor devices. In contrast, this paper presents a framework for the contact-free, real-time estimation of energy expenditure, applicable to daily living scenarios. This is a new application in real-time computer vision. We demonstrate the effectiveness and the benefits of utilising a basic set of features and evaluate the resulting framework on the challenging SPHERE-calorie dataset. To ensure accurate evaluation, automated estimates are compared against a simultaneously taken indirect calorimetry ground truth based on per breath gas exchange. Following detailed experiments, we conclude that the proposed real-time vision pipeline is suitable for monitoring physical activity levels in a controlled environment with higher accuracy than the commonly used manual estimation via metabolic lookup tables (METs), whilst being significantly faster than existing automated methods.