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AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design.

Walaa N Ismail1,2, Hessah A Alsalamah3,4, Mohammad Mehedi Hassan3

  • 1Department of Management Information Systems, College of Business Administration, Al Yamamah University, 11512, Riyadh, Saudi Arabia.

Heliyon
|February 28, 2023
PubMed
Summary

This study introduces AUTO-HAR, an automated system using Genetic Optimization Algorithm (GA) for designing Convolutional Neural Networks (CNNs) for Human Activity Recognition (HAR). It achieves high accuracy by optimizing CNN architectures, overcoming manual design limitations.

Keywords:
CNN topologyConvolution neural networksDeep learningEvolutionary neural network searchGenetic algorithmsHuman activity recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Convolutional Neural Networks (CNNs) excel in time-series analysis for Human Activity Recognition (HAR).
  • Manual design of CNN architectures for HAR is complex, time-consuming, and prone to errors.
  • Neural Architecture Search (NAS) offers automated optimization, overcoming human limitations.

Purpose of the Study:

  • To develop an efficient automated framework (AUTO-HAR) for designing optimal CNN architectures for HAR tasks.
  • To leverage evolutionary algorithms, specifically the Genetic Optimization Algorithm (GA), for NAS in HAR.
  • To introduce a novel encoding schema and an expanded search space for improved architecture discovery.

Main Methods:

  • Utilized the Genetic Optimization Algorithm (GA) for automated CNN architecture selection.
  • Proposed a novel encoding schema and an expanded search space for HAR-specific NAS.
  • Evaluated the AUTO-HAR framework on three benchmark datasets: UCI-HAR, Opportunity, and DAPHNET.

Main Results:

  • The AUTO-HAR framework achieved high accuracy in human activity recognition.
  • Average accuracies reached 98.5% (±1.1%) on UCI-HAR, 98.3% on Opportunity, and 99.14% (±0.8%) on DAPHNET.
  • The proposed method demonstrates efficient and effective HAR through optimized CNN architectures.

Conclusions:

  • AUTO-HAR successfully automates the design of high-performance CNNs for HAR.
  • The GA-driven NAS approach with a novel search space enhances HAR accuracy.
  • This framework offers a robust solution for complex HAR challenges, surpassing manual design limitations.