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Individuals can cheat activity trackers for incentives, but retraining classifiers with deceptive data significantly improves accuracy in recognizing true activities, making systems more robust against fraud.

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Area of Science:

  • Human-Computer Interaction
  • Machine Learning
  • Behavioral Science

Background:

  • Activity recognition technology is increasingly used in healthcare for incentives, motivating users to manipulate data.
  • Existing activity recognition systems are vulnerable to deceptive behaviors aimed at exploiting incentive programs.

Purpose of the Study:

  • To develop and evaluate a novel method for enhancing the robustness of smartphone-based activity recognition against user deception.
  • To investigate the effectiveness of retraining activity classifiers with data from deceptive behaviors.

Main Methods:

  • Recruited 14 subjects to intentionally deceive a smartphone-based activity classifier.
  • Retrained the classifier using motion data collected during successful deception attempts.
  • Iteratively retrained the classifier until subjects could no longer deceive it.

Main Results:

  • Classifiers trained on normal activity data achieved ~38% accuracy.
  • Classifiers retrained with deceptive behavior data achieved ~84% accuracy.
  • Effectiveness of deception varied among individuals, with some unable to deceive the system and others requiring multiple retraining rounds.

Conclusions:

  • Learning an individual's deceptive patterns enhances the classifier's ability to detect deception from others.
  • Incorporating data from deceptive behaviors significantly improves the robustness of activity recognition systems against fraud.
  • This approach offers a viable strategy to make current activity recognition technology more resilient to user manipulation.