Automatic energy expenditure measurement for health science

Çatal, Çağatay; Akbulut, Akhan

doi:10.1016/j.cmpb.2018.01.015

Cited by 9 publications

(13 citation statements)

References 32 publications

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“…Personalization Munguia-Tapia developed a feature ScaledHR to normalize HR, which is very dependent on each subject's fitness level: "this normalization, between resting and maximal heart rate, helps to minimize the inter-individual variations in heart rate values". Altini et al [11,11,13] [41] and with three devices MAE of 0.526 MET [29]. All the results are better than those obtained using only commercial devices, which suggests there is room for improvement for EEE algorithms for commercial smartwatches and wristbands.…”

Section: Specific Approaches and Resultsmentioning

confidence: 91%

“…There are only a few direct comparisons of different methods in the literature, which were achieved by the authors re-implementing methods of other authors such as Cvetković et al [40] who reimplemented the one from Altini et al [14], or using the same dataset such as Catal et al [29] and Gjoreski et al [53]. Variety of activities It is hard to compare a piece of EEE research with few activities to one with a large number of them.…”

Section: Specific Approaches and Resultsmentioning

confidence: 99%

“…The same year, Catal et al [29] improved the work of Gjoreski et al [53] using a Boosted Decision Tree Regression algorithm. They designed a cloud-based EE system that is queried using a web service to obtain EEE given the data from the user.…”

Section: Specific Approaches and Resultsmentioning

confidence: 99%

“…A fundamental context is the recognized activity, which is included in all context-specific methods. Additionally proposed ones are physiological data structured in a hierarchy [126] or discrete intervals (e.g., HR, BR, ST is discretized into low, medium and high) [29,52,53]. The estimations of all context regression models are aggregated into the final result.…”

Section: Eee Methodsmentioning

confidence: 99%

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A Survey on Energy Expenditure Estimation Using Wearable Devices

2020

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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.

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Section: Specific Approaches and Resultsmentioning

confidence: 91%

Section: Specific Approaches and Resultsmentioning

confidence: 99%

Section: Specific Approaches and Resultsmentioning

confidence: 99%

Section: Eee Methodsmentioning

confidence: 99%

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A Survey on Energy Expenditure Estimation Using Wearable Devices

2020

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“…Staudenmayer et al [16] proposed two artificial neural networks (ANN) for physical activity recognition and EE estimation respectively. Catal et al [17] combined the boosted decision tree regression (BDTR) algorithm and the median aggregation algorithm to improve the EE estimation accuracy. Cvetković et al [18] proposed a real-time activity monitoring and EE estimation algorithm with a smartphone and a wristband using the random forest (RF) algorithm which took the variations of sensors' location and orientation into considerations.…”

Section: Related Workmentioning

confidence: 99%

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

Sun²,

et al. 2022

IEEE Trans. Biomed. Eng.

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Objective: Energy Expenditure (EE) estimation plays an important role in objectively evaluating physical activity and its impact on human health. EE during activity can be affected by many factors, including activity intensity, individual physical and physiological characteristics, environment, etc. However, current studies only use very limited information, such as heart rate and step count, to estimate EE, which leads to a low estimation accuracy. Methods: In this study, we proposed a deep multibranch two-stage regression network (DMTRN) to effectively fuse a variety of related information including motion information, physiological characteristics, and human physical information, which significantly improved the EE estimation accuracy. The proposed DMTRN consists of two main modules: a multi-branch convolutional neural network module which is used to extract multi-scale context features from electrocardiogram (ECG) and inertial measurement unit (IMU) data, and a two-stage regression module which aggregated the extracted multi-scale context features containing the physiological and motion information and the anthropometric features to accurately estimate EE. Results: Experiments performed on 33 participants show that our proposed method is more accurate and the average root mean square error (RMSE) is reduced by 22.8% compared with previous works. Conclusion: The EE estimation accuracy was improved by the proposed DMTRN model with a well-designed network structure and new input signal ECG. Significance: This study verified that ECG was much more effective than HR for EE estimation and cast light on EE estimation using the deep learning method.

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