Industrial operators control complex technical processes where only semi-automatic control systems are available or complete automation would not be feasible for safety considerations.
The main goal of operator competence development is to reduce the likelihood of human mistakes in semi-automatic industrial systems where active human intervention are required. There are some psychological states in which the probability of mistakes may increase considerably. We want to detect, prevent or completely eliminate these states if it is possible.
We observe the operators during their work to get information about how they behave under stressful situations. We investigate a variety of physiological and biological features, such as pulse or skin's electrical resistance. We are searching for modern, non-invasive methods and devices that can be used for long periods of time with minimal impact on operators' performance.
The goal of this thesis is to design a measurement data-collection system that can be used to analyze and thus optimize the work of operators. By analyzing operators as thoroughly as possible, there is a potential to develop new industrial-organizational psychological procedures that will reduce the risk of human factor in the future.