Introduction:

The UCLA WINMEC (Wireless Internet for Mobile Enterprise Consortium) has been researching the use of wireless communications technologies using Radio Frequency Identification (RFID) with IOT (Internet of Things) to estimate distance between devices/users/assets in indoor environments. This research program is developing a software app for healthcare practitioners (Doctors, nurses, staff, etc.) so that we have refined idea of who came within how much distance of a healthcare provider inside a hospital. A key hypothesis that we are planning to test with this core technology is to question whether the universal rule of 6-foot distance is correct and appropriate or does it vary with the individual, the hospital, the facility, the activity that an individual is involved, their own personal habits, and also is it static or is it dynamic. Our hypothesis that this varies, is dynamic and is influenced by many factors that we don’t fully understand yet, as this pandemic is new and its spread is not yet fully understood, and that we need to study and investigate this further with advanced technologies such as wireless sensors including Bluetooth, RFID and WiFi. This technical approach will also shed light on when and how the various healthcare facilities should re-open (if they are closed) and a better understanding of the risk factors will enable to open in a more systematic manner and with full understanding of the risks.

Our research will therefore also shed light on questions such as does the 6 foot rule depend on the type of facility and the type of care being given, or, does it depend on a specific patient’s condition (that implies that IOT technology can may be usable to track patients – such as RFID-based wrist-bands), etc. Using AI and machine learning, this approach can pre-empt situations of high risk and in-fact can quantify risk for specific actions and within context. The answers to all these questions are not fully understood in the context of a pandemic such as COVID-19, and will be studied.

Objectives:

  • The technology functions as it should; this implies the usage of the internal wireless communications, sensors, bluetooth and RFID technologies within a cell phone are able to estimate accurately distance between people using signal strength and other factors. Use Machine learning and AI to accurately model and ascertain distances between individuals, reducing errors to an acceptable level.
  • Ascertain the value of the app for providing the function needed which is tracking and storing proximity information that can be used to calculate risk for an individual (both transmitting and being infected with COVID-19) and to refine this information based on real time data obtained dynamically.
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 UCLA Samueli Mechanical and Aerospace Engineering

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