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<p>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.</p></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.

Classification of emotions based on electrodermal activity and transfer learning - a pilot study

Journal of Electrical Bioimpedance

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper describes the development, execution and results of an experiment assessing emotions with electrodermal response measurements and machine learning....

Classification of emotions based on electrodermal activity and transfer learning - a pilot study

Publié en ligne: 30 déc. 2021

Pages: 178 - 183

Reçu: 07 déc. 2021

DOI: https://doi.org/10.2478/joeb-2021-0021

Mots clés
Machine learning, transfer learning, assessing emotions, skin conductance

© 2021 Fredrik A. Jacobsen, Ellen W. Hafli, Christian Tronstad, Ørjan G. Martinsen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Classification of emotions based on electrodermal activity and transfer learning - a pilot study

Publié en ligne: 30 déc. 2021

Pages: 178 - 183

Reçu: 07 déc. 2021

DOI: https://doi.org/10.2478/joeb-2021-0021

Mots clésMachine learning, transfer learning, assessing emotions, skin conductance

© 2021 Fredrik A. Jacobsen, Ellen W. Hafli, Christian Tronstad, Ørjan G. Martinsen, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Machine learning, transfer learning, assessing emotions, skin conductance