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Related Concept Videos

Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

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A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
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Hand hygiene01:23

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Related Experiment Video

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FaceGuard: A Wearable System To Avoid Face Touching.

Allan Michael Michelin1, Georgios Korres1, Sara Ba'ara1

  • 1Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.

Frontiers in Robotics and AI
|April 26, 2021
PubMed
Summary
This summary is machine-generated.

Face touching can spread viruses like COVID-19. This study developed FaceGuard, a system using deep learning and an inertial measurement unit (IMU) to predict and prevent face touching with sensory feedback.

Keywords:
IMU-based hand trackingface touching avoidancesensory feedbackvibrotactile stimulationwearable technologies for health care

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Face touching is a common, unconscious behavior that contributes to the spread of infectious diseases, including COVID-19.
  • Public health guidelines recommend avoiding face touching due to transmission through mucous membranes.
  • Previous research indicates frequent face touching across various demographics and activities.

Purpose of the Study:

  • To develop and evaluate FaceGuard, a novel system designed to predict and prevent face touching.
  • To assess the efficacy of deep learning models in classifying hand movements indicative of face touching.
  • To compare the effectiveness of different sensory feedback modalities for timely intervention.

Main Methods:

  • Utilized an inertial measurement unit (IMU) to capture hand movement data.
  • Developed a 1D-Convolutional Neural Network (1D-CNN) for time-series classification of IMU data.
  • Trained the 1D-CNN model on 4,800 trials from 40 participants across diverse activities.
  • Conducted psychophysical experiments to compare visual, auditory, and vibrotactile feedback response times.

Main Results:

  • The 1D-CNN model achieved over 92% prediction accuracy for face touching with less than 550 ms of IMU data.
  • Vibrotactile feedback demonstrated the fastest response time (427.3 ms) compared to auditory (520.97 ms) and visual (561.70 ms).
  • Vibrotactile and auditory feedback resulted in higher success rates for preventing face touching than visual feedback.

Conclusions:

  • FaceGuard effectively predicts hand-to-face movements using deep learning and IMU data.
  • Timely sensory feedback, particularly vibrotactile, can significantly reduce face touching behaviors.
  • The system demonstrates feasibility for real-world application in mitigating disease transmission.