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Transfer Learning for Activity Recognition: A Survey.

Diane Cook1, Kyle D Feuz, Narayanan C Krishnan

  • 1Department of Electrical Engineering and Computer Science, Washington State University, Pullman WA, USA.

Knowledge and Information Systems
|September 17, 2013
PubMed
Summary
This summary is machine-generated.

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Transfer learning for activity recognition helps intelligent systems by utilizing connections between datasets. This approach addresses the need for large labeled data, improving performance in diverse situations.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Intelligent systems require human activity recognition.
  • Activity recognition is challenging due to diverse circumstances and data needs.
  • Transfer learning offers a solution by leveraging existing datasets.

Purpose of the Study:

  • To survey recent advances in transfer learning for human activity recognition.
  • To categorize existing transfer-based activity recognition methods.
  • To identify future challenges in the field.

Main Methods:

  • Literature survey of transfer learning in activity recognition.
  • Categorization by sensor modality, environmental differences, data availability, and transferred information.
Keywords:
Activity RecognitionMachine LearningSmart EnvironmentsTransfer Learning

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  • Analysis of current approaches and identification of research gaps.

    • Main Results:

    • Recent advances in transfer learning for activity recognition are highlighted.
    • Approaches are systematically characterized based on key factors.
    • The survey provides a structured overview of the research landscape.

    Conclusions:

    • Transfer learning is crucial for developing robust activity recognition systems.
    • Understanding the nuances of transfer methods is key to progress.
    • Addressing grand challenges will advance the field significantly.