Smart Face Shield: A Sensor-Based Wearable Face Shield Utilizing Computer Vision Algorithms
Purpose – 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.
Method – 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.
Results – With the application of the camera-to-distance to object mathematical concept and tag reference model, the prototype was subjected to unit testing using live feed scenarios. The distance between individuals was measured using several markers drawn on the floor to determine whether it falls within the safe, warning, and unsafe tag reference indicated by color schemes such as green, orange, and red with corresponding conditions. The device also tested its capability to automatically detect the measurement of the object in terms of height and compare its true value against the experimental value by using the percent error formula. The analysis revealed that the experimental values were closer to the acceptable or real values of less than 1.0-meter.
Conclusion – The technical design and unit testing validated the mathematical concepts, specifications, functionality, and machine learning algorithm. The percentage errors yielded a minor error indicating that the detected values were close to the actual or original values predicted in real-time. The SFS device has proven its potential use to become a timely innovation to help lessen the spread of the deadly covid-19 as an alternative to the traditional face mask and face shield.
Recommendations – For future modifications, an integration of a wireless personal thermal scanner would be a great advantage.
Practical Implications – The innovations integrated with the wearable smart-face shield can help users observe social distancing to combat the covid-19. Due to its expensive production costs, producing this kind of device for public consumption is not practical.
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