Automated Detection of Complex System Operator's Condition by Using Non-Contact Vital Sensing

This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose, various well-known data mining tools as well as ones developed previously were used. To be more specific, the following techniques were applied for the mentioned problems: support vector machines, artificial neural networks, fuzzy logic systems and others. However, firstly each person's state was monitored using non-contact vital sensing. Experimental results demonstrated that automatically generated fuzzy rule-based classifiers can properly determine the human condition (and reaction) based on data obtained by non-contact vital sensing using the Doppler sensors introduced earlier. Besides, these fuzzy logic systems outperformed alternative well-known data mining tools. Thus, more complex problems related to the automated detection of an operator's condition can be solved in the same manner.