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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12128/20987
Title: Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods
Authors: Romaniszyn-Kania, Patrycja
Pollak, Anita
Bugdol, Marcin D.
Bugdol, Monika N.
Kania, Damian
Mańka, Anna
Danch-Wierzchowska, Marta
Mitas, Andrzej W.
Keywords: affective state analysis; electrodermal activity; emotional response; emotional response; signal analysis
Issue Date: 2021
Citation: Sensors, 2021, Vol. 21, art. no. 4853
Abstract: Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%.
URI: http://hdl.handle.net/20.500.12128/20987
DOI: 10.3390/s21144853
ISSN: 1424-8220
Appears in Collections:Artykuły (WNS)

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