Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm
| Autor: | Chin-Shyurng Fahn, Yi-Fang Lee, I-Jung Lee, Meng-Luen Wu, Bor-Shing Lin, Wei-Jen Chou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Mean squared error
Computer science Posture 0206 medical engineering convolutional neural network physical activity Walking TP1-1185 02 engineering and technology Kinematics 01 natural sciences Biochemistry Convolutional neural network Article Analytical Chemistry Set (abstract data type) Linear regression energy expenditure Humans multilayer perceptron Electrical and Electronic Engineering Exercise Instrumentation Estimation business.industry Chemical technology 010401 analytical chemistry Pattern recognition 020601 biomedical engineering Atomic and Molecular Physics and Optics 0104 chemical sciences Support vector machine machine learning Multilayer perceptron activity classification Artificial intelligence depth camera Energy Metabolism business Algorithms |
| Zdroj: | Sensors Volume 21 Issue 12 Sensors, Vol 21, Iss 4216, p 4216 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21124216 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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