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Real-Life Validation of Emotion Detection System with Wearables

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13259)


Emotion recognition in real life is challenging since training machine learning models requires many annotated samples with experienced emotions. Although collecting such data is a difficult task, we may improve the process by utilizing a pre-trained model detecting emotional events. We conducted a study to test whether employing machine learning models that detect intense emotions to trigger self-assessments collects more data than triggering self-reports randomly. We have examined the performance of three models on 13 participants for three months. Results show that our models enhance the data collection and provide on average 21% more emotionally annotated data in the general setup. The personalized model improves the collection even more – by up to 38%.


This work was partially supported by the National Science Centre, Poland, project no. 2020/37/B/ST6/03806; by the statutory funds of the Department of Artificial Intelligence, Wroclaw University of Science and Technology; by the Polish Ministry of Education and Science – the CLARIN-PL Project.

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Change history

  • 24 May 2022

    In an older version of this paper, there was an error in the cited reference no. 12. This has been corrected.


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Correspondence to Dominika Kunc .

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Kunc, D., Komoszyńska, J., Perz, B., Kazienko, P., Saganowski, S. (2022). Real-Life Validation of Emotion Detection System with Wearables. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham.

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