Department of Automobile and Manufacturing Technologies, Faculty of Manufacturing Technologies with a Seat in Prešov, Technical University of Kosice, Bayerova 1, 080 01 Prešov, Slovakia.
Department of Computer Science, Faculty of Electronics and Information Technologies, Sumy State University, Rimsky-Korsakov, 2, 44007 Sumy, Ukraine.
Int J Environ Res Public Health. 2020 Jan 2;17(1):326. doi: 10.3390/ijerph17010326.
The paper presents the results of the development of the cardio-forecasting technology, which introduces a new method to monitor the state of human-operator, which is characteristic for the given production conditions and for individual operators, to predict the moment of exhaustion of his/her working capacity. The work aims to demonstrate the unique, distinctive features of the cardio-forecasting technology for predicting an individual limit of his/her working capacity for each person. A unique methodology for predicting individually for each person the moment when he/she reaches the limit of his/her working capacity is based on a spectral analysis of a human phonocardiogram in order to isolate the frequency component located at the heart contraction frequency. The trend of the amplitude of this component is approximated by its model; consequently, the coefficients of the trend model are determined. They include the operator's operating time until his/her working capacity is exhausted. A methodology for predicting the moment when he/she reaches the limit of his/her working capacity for each person individually and assessment based on this degree of criticality of their condition will be realized as a software application for smartphones using the Android operating system.
本文介绍了心肺预测技术的研究成果，该技术引入了一种新的方法来监测人类操作者的状态，这种方法在给定的生产条件和个体操作者中具有独特的特征，可以预测其工作能力耗尽的时刻。本工作旨在展示心肺预测技术在预测每个人的个体工作能力极限方面的独特、独特的特点。为了从人类心音图中分离出位于心脏收缩频率的频率分量，以预测每个人达到工作能力极限的时刻，提出了一种独特的方法，对每个人进行单独预测。通过对该分量的幅度进行频谱分析，用其模型来逼近该分量的趋势，从而确定趋势模型的系数。它们包括操作员在工作能力耗尽之前的操作时间。将基于这种对其状况的临界程度的预测每个人达到工作能力极限的时刻的方法实现为使用 Android 操作系统的智能手机的软件应用程序。
