Qubiz wins R&D Project of the Year for the 2nd time in a row
For the second year in a row, Qubiz has won the R&D Project of the Year at the ANIS Gala. This time we entered with a project we developed for xim Ltd. called Lifelight.
ANIS (Employers’ Association of the Software and Services Industry in Romania) is the largest association that supports the development of the Romanian software and services industry. Likewise, the ANIS Gala is the most relevant event where the best IT companies and projects in Romania are publicly recognised.
So it’s kind of a big deal to be among these companies, and it’s especially so given the category that we won: the research & development award. This means Qubiz is at the forefront of Romanian innovation in IT. It means we invest a lot in applying emerging technologies and that we’re pretty good at it.
Join our newsletter: 250+ tech leaders receive quarterly inspiring positive news, insightful tech stories, and updates from our work. Learn more.
About the project
Lifelight is a technology that can measure your pulse, respiration rate, blood pressure, and soon oxygen saturation, just by looking into the camera on a smartphone or tablet device for up to 40 seconds. Lifelight uses statistical and machine learning models to interpret the data from users’ faces to deliver accurate vital signs.
How?
When your heart beats, your skin “micro blushes” red. Undetectable by the human eye, Lifelight’s algorithms capture these tiny changes in colour, cleans up the signals and converts them into vital sign measurements.
Lifelight is CE marked and the result of one of the largest digital physiological studies of its kind — an 8,500 patient clinical study at Portsmouth Hospitals Trust in the UK, recording over 1 million heartbeats.
The solution is 100% contactless, no peripherals are needed and thus can limit cross-contamination! Lifelight’s vision is to convert every ordinary smartphone or tablet into a self-monitoring healthcare device.
Our involvement is both on the front and back end of the app and developing and perfecting the algorithms. The app uses the device’s camera to fetch pixels of the patients’ faces and converts them into a stream of red, green and blue numbers.
The colour signals captured can then be filtered using different techniques such as Independent Component Analysis in order to remove the noise. To make things easier for the users, we developed a face tracking feature so that the camera maintains focus and compensates for the movements of the user. To improve the signal quality, we employed a way to control the camera’s white balance and exposure bias and to detect face “landmarks” and points of interest.
Currently in development is a way of measuring oxygen saturation values (Sp02), a feature that will further support remote patient monitoring.
Join our newsletter: 250+ tech leaders receive quarterly inspiring positive news, insightful tech stories, and updates from our work. Learn more.
