Identification of the human operator stress conditions

Authors

  • Igor Kolosov
  • Oksana Polyvoda
  • Hanna Rudakova

DOI:

https://doi.org/10.34185/1562-9945-5-130-2020-02

Keywords:

аналіз даних, ідентифікація стану, метод найменших квадратів, оцінка параметрів, динамічний об’єкт, рівень напруженості, спостереження, вимірювання

Abstract

Performing their professional duties, people often find themselves in a situation where you need to make a decision. In this case, the stress conditions of the operator tends to increase when managing any process in real time, especially in lack of time. Constant monitoring of the level of the human stress conditions allows to identify in advance the pre-stress and stress conditions and to take certain measures to stabilize the situation. Human condition can be determined by biophysical parameters that change during different operations and can be measured using sensors. Based on the analysis of data recorded on the results of measurements, it is possible to identify the level of the human stress condition and to assess the complexity of the operational situation. To identify model parameters in real time, it is advisable to use repetitive procedures that allow to obtain an estimate of model parameters when new measurements are received. The purpose of the article is to evaluate the parameters of the mathematical model based on the analysis of the recorded biophysical parameters of the human organism, and to evaluate the possibility of their use for the identification of stress conditions. The model parameter estimates are calculated for three dynamic human-specific processes: cardiogram, rheogram, pneumogram. Observation data were obtained for two cases: in a calm (background) conditions of a human and in a stressful (tense) conditions of the same person. The analysis of the results showed that the use of the least squares method allows to obtain estimates of the parameters of the dynamic object model based on the processing data from the monitoring subsystem, which can be used for real-time diagnostics. This opens the way to detect overloads in a timely manner and prevent extreme operating conditions. It is advisable to study the regularity of change of model parameters for different types of people and at different level of stress conditions.

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Published

2020-05-04