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Intelligent system for human activity recognition in IoT environment.

Hassan Khaled1, Osama Abu-Elnasr1, Samir Elmougy1

  • 1Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.

Complex & Intelligent Systems
|November 15, 2021
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Summary
This summary is machine-generated.

This study introduces 1D-HARCapsNet, an enhanced capsule neural network model for recognizing human daily activities in IoT environments. The model significantly improves performance over conventional methods, achieving 98.67% accuracy.

Keywords:
Capsule neural networkDecision support systemHuman activity recognitionIntelligent systemIoT

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Machine learning adoption is increasing across various fields, influencing daily decisions.
  • Recognizing human daily activities in complex Internet of Things (IoT) environments is a growing challenge.

Purpose of the Study:

  • To propose an enhanced capsule neural network model, 1D-HARCapsNet, for accurate human daily activity recognition.
  • To validate the proposed model's effectiveness in a complex IoT setting.

Main Methods:

  • Developed an enhanced capsule neural network model (1D-HARCapsNet) comprising convolution, primary capsule, activity capsule flat, and output layers.
  • Utilized the WISDM dataset, collected via smart devices, for model validation.
  • Applied the random-SMOTE algorithm for data normalization to address dataset imbalance.

Main Results:

  • The proposed 1D-HARCapsNet achieved high performance metrics: 98.67% accuracy, 98.66% precision, 98.67% recall, and a 0.987 F1-measure.
  • Demonstrated a significant performance enhancement compared to the Conventional CapsNet, which obtained 90.11% accuracy.

Conclusions:

  • The 1D-HARCapsNet model shows strong potential and effectiveness for human daily activity recognition in IoT environments.
  • The proposed model offers a substantial improvement over existing capsule network architectures for this task.