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Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition.

Chih-Ta Yen1, Jia-Xian Liao2, Yi-Kai Huang2

  • 1Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a wearable device using deep learning for human activity recognition (HAR), accurately identifying daily movements. This technology aids in rehabilitation assessment for individuals with limited mobility.

Keywords:
convolutional neural network (CNN)deep-learningfeature fusionhuman activity recognition (HAR)inertial sensorwearable device

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Machine Learning

Background:

  • Accurate human activity recognition (HAR) is crucial for health monitoring and rehabilitation.
  • Existing HAR systems often lack accuracy or require complex setups.
  • Wearable devices offer a promising solution for unobtrusive activity monitoring.

Purpose of the Study:

  • To develop and validate a wearable device for recognizing six daily activities using a deep learning algorithm.
  • To assess the accuracy and reliability of the proposed HAR system.

Main Methods:

  • A wearable device with a single-board computer and six-axis sensors was developed.
  • A deep learning algorithm utilizing parallel convolutional neural networks (CNNs) with feature fusion was employed.
  • The system was trained and validated using the UCI HAR dataset and self-recorded data from 21 participants.

Main Results:

  • The system achieved high accuracy in recognizing six activities of daily living.
  • Accuracy rates were 97.49% (UCI dataset) and 96.27% (self-recorded data).
  • Tenfold cross-validation yielded accuracies of 99.56% and 97.46%, respectively.

Conclusions:

  • The proposed CNN architecture demonstrates high performance for HAR.
  • The wearable device is effective for activity recognition in diverse datasets.
  • This technology has potential applications in rehabilitation assessment for individuals unable to perform strenuous exercise.