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Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity

Wing W Y Ng1, Shichao Xu1, Ting Wang1

  • 1Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|March 19, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for recognizing activities in smart homes using binary sensor data. The proposed approach achieves high accuracy, enhancing health and social care services through reliable activity recognition.

Keywords:
activity recognitionautoencoderlocalized generation errorradial basis function neural networksmart homesstochastic sensitivity

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Ubiquitous Computing

Background:

  • The Internet of Things (IoT) is increasingly integrated into daily life, particularly with smart home devices.
  • Home-based activity recognition is crucial for leveraging IoT data to improve health and social care services.
  • Recognizing activities involves analyzing interactions between individuals and their environment via embedded sensors.

Purpose of the Study:

  • To propose a novel method for home-based activity recognition using binary sensor data.
  • To enhance feature extraction from binary sensor data for improved activity recognition.
  • To improve the generalization capability and robustness of activity recognition models.

Main Methods:

  • Utilized binary data from anonymous sensors (pressure, contact, passive infrared).
  • Proposed a radial basis function neural network (RBFNN) combined with a localized stochastic-sensitive autoencoder (LiSSA).
  • Employed an autoencoder (AE) to convert binary inputs to continuous, extracting deeper features and minimizing training error and stochastic sensitivity.

Main Results:

  • The proposed LiSSA-RBFNN method demonstrated superior performance across four benchmark datasets (OrdonezA, OrdonezB, Ulster, vanKasterenADL).
  • Achieved high accuracy rates: 98.35% on OrdonezA, 86.26% on OrdonezB, 96.31% on Ulster, and 92.31% on vanKasterenADL.
  • Outperformed established methods like Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLPNN), and Random Forest.

Conclusions:

  • The LiSSA-RBFNN method is highly effective for home-based activity recognition using binary sensor data.
  • The approach offers enhanced accuracy and robustness, making it suitable for practical health and social care applications.
  • This work contributes a significant advancement in sensor-based activity recognition within smart home environments.