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Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm

1
Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 237303, Taiwan
2
College of Electrical Engineering and Computer Science, National Taipei University, New Taipei City 237303, Taiwan
3
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
4
Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 251301, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Mario Munoz-Organero
Sensors 2021, 21(12), 4216; https://doi.org/10.3390/s21124216
Received: 25 May 2021 / Revised: 14 June 2021 / Accepted: 18 June 2021 / Published: 19 June 2021
(This article belongs to the Section Wearables)
Insufficient physical activity is common in modern society. By estimating the energy expenditure (EE) of different physical activities, people can develop suitable exercise plans to improve their lifestyle quality. However, several limitations still exist in the related works. Therefore, the aim of this study is to propose an accurate EE estimation model based on depth camera data with physical activity classification to solve the limitations in the previous research. To decide the best location and amount of cameras of the EE estimation, three depth cameras were set at three locations, namely the side, rear side, and rear views, to obtain the kinematic data and EE estimation. Support vector machine was used for physical activity classification. Three EE estimation models, namely linear regression, multilayer perceptron (MLP), and convolutional neural network (CNN) models, were compared and determined the model with optimal performance in different experimental settings. The results have shown that if only one depth camera is available, optimal EE estimation can be obtained using the side view and MLP model. The mean absolute error (MAE), mean square error (MSE), and root MSE (RMSE) of the classification results under the aforementioned settings were 0.55, 0.66, and 0.81, respectively. If higher accuracy is required, two depth cameras can be set at the side and rear views, the CNN model can be used for light-to-moderate activities, and the MLP model can be used for vigorous activities. The RMSEs for estimating the EEs of standing, walking, and running were 0.19, 0.57, and 0.96, respectively. By applying the different models on different amounts of cameras, the optimal performance can be obtained, and this is also the first study to discuss the issue. View Full-Text
Keywords: activity classification; convolutional neural network; depth camera; energy expenditure; machine learning; multilayer perceptron; physical activity activity classification; convolutional neural network; depth camera; energy expenditure; machine learning; multilayer perceptron; physical activity
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MDPI and ACS Style

Lin, B.-S.; Lee, I.-J.; Fahn, C.-S.; Lee, Y.-F.; Chou, W.-J.; Wu, M.-L. Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm. Sensors 2021, 21, 4216. https://doi.org/10.3390/s21124216

AMA Style

Lin B-S, Lee I-J, Fahn C-S, Lee Y-F, Chou W-J, Wu M-L. Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm. Sensors. 2021; 21(12):4216. https://doi.org/10.3390/s21124216

Chicago/Turabian Style

Lin, Bor-Shing, I-Jung Lee, Chin-Shyurng Fahn, Yi-Fang Lee, Wei-Jen Chou, and Meng-Luen Wu. 2021. "Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm" Sensors 21, no. 12: 4216. https://doi.org/10.3390/s21124216

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