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Using Smartwatches to Detect Face Touching.

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Summary
This summary is machine-generated.

Smartwatch app accurately detects face touching (FT) using machine learning, offering a new way to reduce disease transmission. This technology helps monitor and limit behaviors that spread respiratory illnesses like COVID-19.

Keywords:
COVID-19accelerometerface touchingmachine learningrespiratory illnessessmartwatchwearables

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Public Health

Background:

  • Facial self-touches can transmit diseases, especially during outbreaks.
  • Behavioral interventions are crucial for reducing respiratory illness spread, even with vaccines.
  • Smartwatches offer a novel platform for monitoring health-related behaviors.

Purpose of the Study:

  • To develop a smartwatch application for accurate face touching (FT) detection.
  • To identify motion signatures indicative of facial self-touches using machine learning.
  • To assess the potential of wearable technology in limiting disease transmission vectors.

Main Methods:

  • Developed a smartwatch application on a Samsung Galaxy Watch to collect accelerometer data.
  • Extracted data features from time windows ranging from 2 to 16 seconds.
  • Evaluated machine learning models (logistic regression, SVM, decision trees, random forest) for FT and individual activity recognition (IAR).

Main Results:

  • Logistic regression achieved the highest accuracy (0.93) for distinguishing face touching (FT) from non-face touching (NFT) at a 5s window.
  • Random forest showed the best performance for individual activity recognition (IAR) with 70% accuracy at a 9s window.
  • All tested machine learning models demonstrated accuracy in recognizing general face-touching movements.

Conclusions:

  • Wearable devices integrated with machine learning effectively detect facial touches.
  • This technology has significant implications for public health during outbreaks by potentially reducing transmission via facial contact.
  • Smartwatch-based detection of face touching can be a valuable tool for behavioral interventions.