Seeking Startup Investors and Co-founders to help locally build and assemble Covid Screening Cameras:

Automatically COUNT & TEST for COVID-19 using the power of Artificial Intelligence in an autonomous camera.

The camera scans 12 points on the head, including a point at the inner eye that is the most accurate for temperature readings. The measurements are within a half-degree of accuracy using a standard thermal sensor MLX90614 and camera.

(From high level to Technical: The plurality of acquired images having different wavelength compositions that are obtained generally simultaneously. The system includes at least two optical channels that are spatially and spectrally different from one another. Each of the at least two optical channels are configured to transfer IR radiation incident on the optical system towards an optical FPA unit comprising at least two detector arrays disposed in the focal plane of two corresponding focusing lenses.)

Skyrocket visits in cleanroom 100 settings to your business – currently patent pending a coronavirus screening laser curtain - ensuring the safety of the premises, be it airports, tram terminals, train stations

How does it work? Effectively we use a machine learning model to study live pictures of incoming and outgoing clients/patients at hospitals / members of staff to alert as to temperature and facial pigmentation consistent with coronavirus symptoms detectable, we are also working on a biosphere invention that will detect electrostatic patterns from the human biodome allowing further screening enhancements.

For now our covid screening camera automatically counts incoming and outgoing guests and tabulates them by temperature, and measures the temperature. This should provide sufficient alerting so as to shield the public from infection.

Covid19 prevention is a significant problem currently that is significantly detrimental to very time and money consuming. With our COVIDSCREEN AI Video screener, covid screening is rendered cost effective – and faster to get results.

Some of the code was opensourced and machine learning training models were built in Google Cloud Platform Cloud architecture.

for the autonomous mobile tablet version we can piggyback off the nanosense so as to gain more sensor data for the biosphere project, possibly a serverless application can be built using the very basis for the nanosense architecture, this requires more looking into, for now we have a prototype.