A Survey on Energy Expenditure Estimation Using Wearable Devices

Abstract

Human Energy Expenditure (EE) is a valuable tool for measuring physical activity and its impact on our body in an objective way. To accurately measure the EE, there are methods such as doubly labeled water and direct and indirect calorimetry, but their cost and practical limitations make them suitable only for research and professional sports. This situation, combined with the proliferation of commercial activity monitors, has stimulated the research of EE estimation (EEE) using machine learning on multimodal data from wearable devices. The article provides an overview of existing work in this evolving field, categorizes it, and makes publicly available an EEE dataset. Such a dataset is one of the most valuable resources for the development of the field but is generally not provided by researchers due to the high cost of collection. Finally, the article highlights best practices and promising future direction for designing EEE methods.