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Area of Science:

  • Computer Science
  • Signal Processing

Background:

  • Human action recognition using WiFi Channel State Information (CSI) offers non-intrusive sensing for healthcare, smart environments, and security.
  • The reliability of CSI-based action recognition systems is heavily dependent on dataset quality and evaluation protocols.

Purpose of the Study:

  • To identify and analyze a critical data leakage issue in a widely used WiFi CSI benchmark dataset.
  • To demonstrate the impact of improper data partitioning on the performance metrics of human action recognition models.

Main Methods:

  • Investigated a prevalent WiFi CSI benchmark dataset for data partitioning errors.
  • Retrained benchmarked action recognition models using corrected data splitting techniques that ensure individual separation between training and testing sets.
  • Evaluated model performance under rigorous, leakage-free conditions.

Main Results:

  • A significant data leakage issue was uncovered due to improper separation of individuals between training and testing sets in the benchmark dataset.
  • Retrained models exhibited a substantial decrease in accuracy when data partitioning correctly excluded individual overlap, indicating inflated prior performance.
  • The study confirmed that models were learning individual-specific features rather than generalizable action patterns.

Conclusions:

  • Rigorous data partitioning is crucial for developing reliable WiFi CSI-based human action recognition systems.
  • Existing performance benchmarks for some WiFi CSI datasets may be overly optimistic due to data leakage.
  • Recommendations are provided for mitigating data leakage and improving future research in this domain.