Open AccessDevelopment of a multi-sensor analytical trainable system for non-invasive evaluation of adaptedness status of hazardous occupation specialists
Author(s) -
A. Yu Zaitseva,
L. P. Kislyakova,
Yu. Ya. Kislyakov,
S. A. Avduchenko
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1400/3/033022
Subject(s) - hazardous waste , computer science , order (exchange) , artificial intelligence , machine learning , engineering , waste management , finance , economics
Several studies have been conducted in order to develop a new method of non-invasive diagnostics of performance ability of hazardous occupation specialists in terms of the exhaled air condensate parameters with the use of an artificial multi-sensor trainable diagnostic system. The method is based on applying an array of poly-selective electrochemical sensors with cross sensitivity to main physiologically important components of a subject medium. Training of the system was implemented according to the principle of grouping test persons using mathematical methods of data processing and correlation with real adaptedness level data obtained during medical and biological examinations. After having been trained, the analytical system enabled subsequent recognition of the “image” of a testee and assigning them to an adaptedness level group. The results obtained prove feasibility of the new multi-sensor trainable complex, its suitability for adaptedness status evaluation of hazardous occupation specialists.