Russia, Lomonosov Moscow State University
We propose low-cost and non-invasive solution for everyday health monitoring applying artificial intelligence to analyze urine transmission spectra. We use the hypothesis that the state of our urine is related to the state of our health. Using advanced methods of machine learning to analyze the transmission spectra of urine, we learn about the state of human health. Our solution allows you to detect deviations from the norm, warns the user about a possible disease for timely treatment to a doctor. Our product belongs to the promising direction of development in the field of health care implementing telemedicine approaches. To obtain spectral data, we developed a special IoT gadget that is embedded in the toilet or can be used as an independent device. The device collects measurement results and, using artificial intelligence and Microsoft Azure cloud technologies, determines the current state of the patient's health. To control the device, a UWP-application has been developed, in which the user can view the results and statistics of analyzes. Artificial intelligence determines deviations from the norm, and the user immediately receives a notification with a recommendation to consult a doctor for an examination.
Our team consists of MSU students. We explore the possibilities of using artificial intelligence to analyze spectral data and obtain non-trivial information about complex systems. Previously, we successfully applied methods of machine learning to determine the tastes of wine and coffee by optical spectra. Members of our team possess versatile skills in the field of modern technologies. Balashov Igor - PhD student of the Physics Faculty of Moscow State University. He has experience in the development of robotics and additive technologies, manufacturing of nanostructures, optical spectroscopy, nanophotonics and plasmonics. Ruslan Gabdullin - Master of the Faculty of Computational Mathematics and Cybernetics, Moscow State University. He has a profound knowledge of mathematical disciplines: probability theory, optimization methods, numerical methods, game theory, measure theory, random processes, risk theory. Advanced C / C ++, Python, R. Pavleev Ivan - student of the Physics Faculty of Moscow State University. He has knowledge in the field of medical physics. Prizes on hackathons. Possesses expertise in matters of intellectual property, building business processes, planning a business model, attracting customers.