GEOMETRIC VISUALIZATION OF THE PROBLEM OF MEDICAL DIAGNOSTICS BIOMETRIC DATA OF THE BIOSENSOR PLATFORM

Author(s): 

Igor I. Koltunov1, Anton V. Panfilov2, Ivan A. Poselsky3*, Nikolay N. Chubukov2, Ivan V. Krechetov4, Arkadiy A. Skvortsov5

Affiliation(s): 

1Department of Information Systems and Technologies, Moscow Polytechnic University, 38 Bolshaya Semenovskaya Str., Moscow, Russian Federation

2Tradition Group LTD, 11 Timura Frunze Str., Moscow, Russian Federation

3NTC Automated Technical Systems, Moscow Polytechnic University, 38 Bolshaya Semenovskaya Str., Moscow, Russian Federation

4Office of Scientific Research and Dev elopment, Moscow Polytechnic University, 38 Bolshaya Semenovskaya Str., Moscow, Russian Federation

5Department of Mechanics of Materials, Moscow Polytechnic University, 38 Bolshaya Semenovskaya Str., Moscow, Russian Federation

*Corresponding Author E-mail: v-pos@yandex.ru

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The aim of the work is to study the algorithmic solutions of non-invasive medical remote monitoring of human health based on the wearable biosensor platform in the mode of everyday wear. The relevance of the topic is due to the need to improve the quality and reduce the cost of medical services provided to the population due to the release of medical personnel from the painstaking and lengthy analysis of complaints, anamnesis, examination, laboratory research, shortening the time for specialists to select, search and summarize information, giving the opportunity about the adoption of health of the patient on the basis of visualization, convenient for visual perception, visualized by analytical methods of geometry concise generalized by diagnostic picture. The proposed method is implemented on the basis of ideological recognition of geometrized images. The work responds to global trends in contextual visualization of large streams of heterogeneous data.