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Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances.

Enrique Garcia-Ceja1, Ramon F Brena2

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Summary
This summary is machine-generated.

This study introduces a new method for Human Activity Recognition (HAR) personalization. It effectively uses community data to train user-specific models with limited personal data, improving service personalization.

Keywords:
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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Ubiquitous Computing

Background:

  • Human Activity Recognition (HAR) is crucial for personalized services in ambient intelligence.
  • Current HAR models face challenges due to the high cost of labeling training data.
  • General HAR models perform poorly for new users due to inherent biases.

Purpose of the Study:

  • To develop a personalized HAR model requiring minimal labeled data from the target user.
  • To leverage existing community-labeled data to enhance individual user models.
  • To improve the practicality and personalization of HAR systems.

Main Methods:

  • A novel approach combining scarce target-user data with a community-labeled dataset.
  • Training personalized models that adapt to individual user characteristics.
  • Comparative analysis against general and user-dependent models.

Main Results:

  • The proposed personalized model significantly outperformed general and user-dependent models.
  • Effectiveness demonstrated particularly in scenarios with limited labeled data for the target user.
  • Successful adaptation to individual user characteristics without extensive personal data.

Conclusions:

  • The developed method offers an efficient solution for personalized HAR with scarce data.
  • This approach enhances the personalization of ambient intelligence services.
  • It addresses the data labeling bottleneck in practical HAR applications.