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

Bor-Shing Lin1, I-Jung Lee1,2, Chin-Shyurng Fahn3

  • 1Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 237303, Taiwan.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an accurate energy expenditure (EE) estimation model using depth cameras and physical activity classification. Optimal results are achieved with specific camera setups and machine learning models like multilayer perceptron (MLP) and convolutional neural network (CNN).

Keywords:
activity classificationconvolutional neural networkdepth cameraenergy expendituremachine learningmultilayer perceptronphysical activity

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Area of Science:

  • Biomechanics
  • Computer Vision
  • Health Informatics

Background:

  • Modern lifestyles often involve insufficient physical activity, impacting overall health.
  • Accurate estimation of energy expenditure (EE) is crucial for developing effective exercise plans.
  • Existing methods for EE estimation have limitations that this study aims to address.

Purpose of the Study:

  • To propose an accurate energy expenditure (EE) estimation model utilizing depth camera data and physical activity classification.
  • To determine the optimal number and placement of depth cameras for EE estimation.
  • To compare the performance of different machine learning models for EE estimation.

Main Methods:

  • Three depth cameras were positioned at side, rear side, and rear views to capture kinematic data.
  • Support Vector Machine (SVM) was employed for physical activity classification.
  • Linear regression, Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN) models were evaluated for EE estimation.

Main Results:

  • Optimal EE estimation with a single depth camera (side view) was achieved using the MLP model (MAE: 0.55, MSE: 0.66, RMSE: 0.81).
  • For higher accuracy, two cameras (side and rear views) combined with CNN for light-to-moderate activities and MLP for vigorous activities yielded improved results.
  • Specific RMSEs for standing, walking, and running were 0.19, 0.57, and 0.96, respectively, using the optimized camera and model configurations.

Conclusions:

  • The study demonstrates that optimal energy expenditure estimation can be achieved by selecting appropriate machine learning models and camera configurations based on accuracy requirements.
  • This research is the first to investigate the impact of different camera setups and models on EE estimation accuracy.
  • The findings provide a foundation for developing more personalized and effective exercise and lifestyle management strategies.