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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Developing a novel hybrid method based on dispersion entropy and adaptive boosting algorithm for human activity

Mohammed Diykh1, Shahab Abdulla2, Ravinesh C Deo3

  • 1College of Education for Pure Science, University of Thi-Qar, Iraq; Information and Communication Technology Research Group, Scientific Research Centre, Al-Ayen University, Iraq.

Computer Methods and Programs in Biomedicine
|December 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new hybrid model combining hierarchical dispersion entropy (HDE) and Adaptive Boosting with convolutional neural networks (AdaB_CNN) for accurate human activity recognition (HAR) from sensor data, addressing data imbalance issues.

Keywords:
AdaB_ANNHierarchical dispersion entropyHuman activity recognitionJADE

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Area of Science:

  • Sensor data analysis
  • Machine learning for human activity recognition (HAR)

Background:

  • Human activity recognition (HAR) is crucial for applications like healthcare and smart homes.
  • Traditional HAR methods struggle with efficiency, accuracy, and speed.
  • Imbalanced data in HAR significantly hinders satisfactory performance.

Purpose of the Study:

  • To propose a novel hybrid approach for human activity recognition (HAR).
  • To address the challenge of imbalanced data in HAR datasets.
  • To improve the efficiency and accuracy of HAR systems.

Main Methods:

  • A hybrid approach combining hierarchical dispersion entropy (HDE) and Adaptive Boosting with convolutional neural networks (AdaB_CNN).
  • Data segmentation using sliding windows and decomposition into frequency bands.
  • Feature extraction via HDE, dimensionality reduction using Joint Approximate Diagonalization of Eigenmatrices (JADE), and classification with AdaB_CNN.

Main Results:

  • The proposed HDE-based AdaB_CNN model demonstrated superior performance on three public datasets (WISDM, UCI_HAR 2012, PAMAP2).
  • The approach effectively classifies human activities like walking and running.
  • Experimental results show improved HAR capabilities compared to existing methods.

Conclusions:

  • The HDE-based AdaB_CNN model efficiently recognizes human activities from sensor data.
  • This model shows potential for implementation in hardware systems for real-time activity classification.
  • The study successfully addresses limitations in current HAR techniques.