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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Updated: Jun 28, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Semi-supervised ensemble learning for human activity recognition in casas Kyoto dataset.

Ariza-Colpas Paola Patricia1, Pacheco-Cuentas Rosberg1, Shariq Butt-Aziz2

  • 1Universidad de la Costa, Department of Computer Science and Electronics, Barranquilla, Colombia.

Heliyon
|April 24, 2024
PubMed
Summary

This study introduces a novel Semi-supervised Ensemble Learning model for Human Activity Recognition (HAR). The approach enhances smart home safety for the elderly by accurately identifying daily activities using clustering and classification.

Keywords:
Activities of daily livingClassification methodsClusteringEnsemble learningHuman activity recognitionSemi-supervisedSmart home

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human Activity Recognition (HAR) is crucial for applications in health, entertainment, and sports.
  • Smart homes offer potential for elderly care, particularly for those with neurodegenerative diseases.
  • Existing HAR datasets, like the CASAS dataset, provide valuable data for indoor activity identification.

Purpose of the Study:

  • To develop an advanced HAR model using Semi-supervised Ensemble Learning.
  • To leverage distance-based clustering for enhanced activity identification.
  • To improve the accuracy and effectiveness of HAR systems in smart home environments.

Main Methods:

  • A novel model based on Semi-supervised Ensemble Learning was developed.
  • Distance-based clustering analysis was employed to identify distinct activity clusters.
  • Supervised techniques were utilized for the subsequent classification of identified clusters.

Main Results:

  • The proposed model demonstrated promising outcomes in activity identification.
  • Quality metrics analysis indicated favorable results compared to state-of-the-art methods.
  • The integrated framework showed significant potential for HAR applications.

Conclusions:

  • The Semi-supervised Ensemble Learning model offers a robust approach to Human Activity Recognition.
  • This method can significantly enhance the capabilities of smart homes for elder care and safety.
  • Further research can build upon this framework for more sophisticated HAR systems.