Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods
| Autor: | Anita Pollak, Marcin D. Bugdol, Anna Mańka, Marta Danch-Wierzchowska, Monika N. Bugdol, Andrzej W. Mitas, Damian Kania, Patrycja Romaniszyn-Kania |
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| Rok vydání: | 2021 |
| Předmět: |
Male
Emotions 02 engineering and technology TP1-1185 Machine learning computer.software_genre 01 natural sciences Biochemistry Sensitivity and Specificity signal analysis Article Analytical Chemistry Machine Learning 0202 electrical engineering electronic engineering information engineering Verbal fluency test Humans Statistical analysis Cognitive skill Electrical and Electronic Engineering Instrumentation Physical Therapy Modalities Protocol (science) business.industry Chemical technology 010401 analytical chemistry emotional response Atomic and Molecular Physics and Optics electrodermal activity 0104 chemical sciences Knn classifier Test (assessment) 020201 artificial intelligence & image processing Female Artificial intelligence Selection method State (computer science) business Psychology computer affective state analysis |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 21 Issue 14 Sensors, Vol 21, Iss 4853, p 4853 (2021) |
| ISSN: | 1424-8220 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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