Unsupervised-based framework for aged worker's stress detection

The new industry 4.0 paradigm provides for totally automated and interconnected industrial production processes that require greater human-machine interaction. This involves the onset of new problems related to the stress evaluation of the aged worker which is found to operate in new and more complex work contexts. In literature several works for human stress detection are presented, they use above supervised machine learning with high accuracy level detection, but needed a complicated training phase. Moreover, a relevant issue in the field of stress detection lies in the model validation, indeed the commonly questionnaires used to record perceived stress levels are prone to subjective inaccuracies. To reduce this limitation, in this paper an unsupervised machine learning based stress detection system, in which the labels from perceived stress levels are not needed, is presented. It analyses heart rate, galvanic skin response and electrooculogram signals, relevant for the detection of excessive stress and cognitive load. The developed architecture software has been experimented in laboratory contest and preliminary obtained results appear promising.