Creating a Temperature-Tracking Door Locking System for COVID-19; Face2Gene: Using Facial Recognition to Aid in Diagnosing Rare Genetic Disorders
Rein, Amanda, School of Engineering and Applied Science, University of Virginia
Powell, Harry, EN-Elec/Computer Engr Dept, University of Virginia
Rogers, Hannah, Science Technology & Society (STS), University of Virginia
Creating a Temperature-Tracking Door Locking System for COVID-19: This project’s final deliverable is an automated health screening door connecting temperature measurements to the door lock. When a prospective entrant approaches a door outfitted with this system, they will place their wrist under a mounted box, triggering a motion detector and awakening a non-contact temperature sensor. The temperature sensor will take a temperature reading from the person’s wrist, and that reading will be interpreted within a MSP432 microcontroller to determine whether to unlock the door. If the temperature is above a healthy range, the MSP432 will communicate to the door that it should lock and prevent that person’s entry to the space until they can return with a normal temperature reading. At the same time that the MSP432 makes the locking decision, the temperature reading is sent to a Raspberry Pi server, which stores all temperature readings in a database. This database is utilized to render a web dashboard for stakeholders to this door locking system. Anyone interested in seeing the number of people allowed to (or prevented from) entering the space, or other statistics on door usage, may find this information on the website.
Face2Gene: Using Facial Recognition to Aid in Diagnosing Rare Genetic Disorders: Face2Gene utilizes facial recognition technology to analyze a “selfie” of a patient, then comes up with a list of most likely diagnoses. With over 6,000 rare genetic disorders that are often accompanied with a variety of subtle but distinct facial cues, diagnosticians need assistance in swiftly and accurately providing a diagnosis for people with rare genetic disorders. As this application has been built on a machine learning model, it has been molded to suit the accuracy and diversity needed by medical professionals and patients alike. As such, Face2Gene provides a rich subject for analysis under the framework social construction of technology (SCOT) because it is an application that has been shaped by human action. This paper will provide background information on Face2Gene, then analyze how the application has grown since its launch in 2014 under the SCOT pillars of interpretative flexibility, wider context, and closure & stabilization.
BS (Bachelor of Science)