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.

中文翻译：

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使用可穿戴设备进行能源支出估算的调查

人体能量消耗 (EE) 是一种有价值的工具，可以客观地衡量身体活动及其对我们身体的影响。为了准确测量EE，有双重标记水和直接和间接量热法等方法，但它们的成本和实际限制使其仅适用于研究和专业运动。这种情况，再加上商业活动监视器的激增，刺激了使用机器学习对来自可穿戴设备的多模态数据进行 EE 估计 (EEE) 的研究。本文概述了这一不断发展的领域中的现有工作，对其进行分类，并公开提供 EEE 数据集。这样的数据集是该领域发展最有价值的资源之一，但由于收集成本高，研究人员通常不提供。最后，