Smart Face Shield: A Sensor-Based Wearable Face Shield Utilizing Computer Vision Algorithms

TL;DR

This paper presents a sensor-based wearable face shield integrated with computer vision algorithms to detect social distancing violations and enhance health safety during COVID-19.

Contribution

It introduces a novel wearable face shield with embedded sensors and computer vision for real-time social distancing monitoring.

Findings

Effective distance detection using camera and OpenCV

Successful implementation of object detection with TensorFlow

Enhanced health safety awareness through wearable technology

Abstract

The study aims the development of a wearable device to combat the onslaught of covid-19. Likewise, to enhance the regular face shield available in the market. Furthermore, to raise awareness of the health and safety protocols initiated by the government and its affiliates in the enforcement of social distancing with the integration of computer vision algorithms. The wearable device was composed of various hardware and software components such as a transparent polycarbonate face shield, microprocessor, sensors, camera, thin-film transistor on-screen display, jumper wires, power bank, and python programming language. The algorithm incorporated in the study was object detection under computer vision machine learning. The front camera with OpenCV technology determines the distance of a person in front of the user. Utilizing TensorFlow, the target object identifies and detects the image or live feed to get its bounding boxes. The focal length lens requires the determination of the distance from the camera to the target object. To get the focal length, multiply the pixel width by the known distance and divide it by the known width (Rosebrock, 2020). The deployment of unit testing ensures that the parameters are valid in terms of design and specifications.