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Updated: May 3, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Cross-person activity recognition using reduced kernel extreme learning machine.

Wan-Yu Deng1, Qing-Hua Zheng2, Zhong-Min Wang1

  • 1School of Computer Science and Technology, Xian University of Posts & Telecommunications, 710121, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 12, 2014
PubMed
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This study introduces TransRKELM, a novel transfer learning model for fast and accurate cross-person activity recognition using mobile accelerometers. It efficiently adapts models to new users, improving performance in pervasive applications.

Area of Science:

  • Computer Science
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Activity recognition using mobile accelerometers is crucial for human-centric applications like healthcare.
  • Accelerometer data distribution varies significantly between users, degrading model performance when applied across individuals.

Purpose of the Study:

  • To develop a fast and accurate cross-person activity recognition model that addresses user variability.
  • To enable efficient adaptation of recognition models to new device users.

Main Methods:

  • Proposed TransRKELM (Transfer learning Reduced Kernel Extreme Learning Machine) model.
  • Utilized RKELM (Reduced Kernel Extreme Learning Machine) for initial model training.
  • Employed OS-RKELM (Online Sequential Reduced Kernel Extreme Learning Machine) for efficient online model updates and adaptation.
Keywords:
Activity recognitionExtreme learning machineReduced kernel extreme learning machineSupport vector machine

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Main Results:

  • The TransRKELM model demonstrates rapid adaptation to new device users.
  • Achieved good recognition performance even with user variability.

Conclusions:

  • TransRKELM offers an effective solution for cross-person activity recognition.
  • The model's ability to quickly adapt classifiers enhances its utility in real-world pervasive applications.