Abstract
This paper describes the development, execution and results of an experiment assessing emotions with electrodermal response measurements and machine learning. With ten participants, the study was carried out by eliciting emotions through film clips. The data was gathered with the Sudologger 3 and processed with continuous wavelet transformation. A machine learning algorithm was used to classify the data with the use of transfer learning and random forest classification. The results showed that the experiment lays a foundation for further exploration in the field. The addition of augmented data strengthened the classification and proved that more data would benefit the machine learning algorithm. The pilot study brought to light several areas to help with the expansion of the study for larger scale assessment of emotions with electrodermal response measurements and machine learning for the benefit of fields like psychology.