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.

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model

“…The subject is placed in a thermally isolated chamber, and the total heat loss from the body is recorded accurately and measured precisely [ 40 ]. Due to the complexity of the equipment required, it is the most expensive and least practical way to measure EE [ 41 ]. The indirect calorimetry is the most widely employed method to measure CO 2 and O 2 [ 40 ].…”

“…In addition, the battery of these indirect techniques consists of two or three hours and is also expensive. DLW is also a gold standard technique for measuring EE, with low error rates over seven to fourteen days [ 41 ]. However, this technique cannot record minute-by-minute information like indirect calorimetry.…”

Deep Multi-Branch Two-Stage Regression Network for Accurate Energy Expenditure Estimation With ECG and IMU Data

“…Previous studies [24] showed that the longer the sliding window used, the smaller the EE estimation error. Besides, according to the test results we found that the EE of the human body fluctuates little in one minute indicating and 1-minute window has been used in [21] [30] [31].…”

“…As the information contained in the discrete features is limited, the accuracy of the EE provided by commercial wearable devices is not accurate enough for some applications such as rehabilitation exercise after a heart attack [5] or heart surgery or professional sports training [6]. Besides, a systematic review [7] on the validity and reliability of commercial wearables in measuring energy expenditure published in 2020 concluded that the EE estimation function of the studied commercial wearable devices including Fitbit, Numerous efforts have been done to improve the accuracy of EE estimation [8]- [24]. However, as most of the existing EE estimation methods were based on machine learning algorithms which need to manually design and select features, their EE estimation accuracy was still unsatisfactory.…”

Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines

“…More recently, increased storage capacity and the availability of raw acceleration data in popular researchgrade accelerometers like the ActiGraph (Troiano et al 2014, Sasaki et al 2016 have led to the development of different approaches to analyzing these data (de Almeida Mendes et al 2018, Farrahi et al 2019, Álvarez-García et al 2020. Use of raw acceleration data can facilitate comparisons across devices brands (de Almeida Mendes et al 2018) while use of machine learning or other advanced modeling techniques can more fully make use of the vast amount of information collected by a triaxial accelerometer, as numerous time-and/or frequency-domain characteristics may be used as model inputs (Farrahi et al 2019).…”