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Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition.

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This study explores deep transfer learning for human activity recognition (HAR) using sensor data. The Maximum Mean Discrepancy (MMD) method, enhanced with center loss, shows promise for improving cross-user HAR accuracy with unlabeled target data.

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Area of Science:

  • Pervasive computing
  • Machine learning
  • Sensor data analysis

Background:

  • Human activity recognition (HAR) using sensor data is crucial in pervasive computing.
  • Deep learning models achieve high accuracy but struggle with cross-user recognition.
  • Limited research exists on transferring deep learning models for HAR, especially with unlabeled target data.

Purpose of the Study:

  • To empirically investigate deep transfer learning for user-independent HAR.
  • To identify optimal transfer learning algorithms for sensor-based HAR.
  • To improve HAR accuracy by addressing feature distribution discrepancies.

Main Methods:

  • Empirical comparison of widely-used transfer learning algorithms.
  • Application of Maximum Mean Discrepancy (MMD) for domain adaptation.
  • Feature distribution analysis and improvement using center loss.

Main Results:

  • Maximum Mean Discrepancy (MMD) identified as the most suitable method for HAR.
  • Feature distribution analysis revealed insights into cross-user HAR challenges.
  • Integration of center loss with MMD significantly improved recognition accuracy.

Conclusions:

  • Deep transfer learning, particularly MMD with center loss, offers a viable solution for user-independent HAR.
  • This study provides valuable insights and guidance for future research in transfer learning for activity recognition.
  • Addressing feature distribution is key to enhancing the practical applicability of HAR systems.