Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods.

Patrycja Romaniszyn-Kania,Anna Mańka,Andrzej W Mitas,Marta Danch-Wierzchowska,Monika N Bugdol,Anita Pollak,Marcin D Bugdol,Damian Kania

doi:10.3390/s21144853

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%.

Highlights

Effective disease management requires a constant search for factors relevant to improving the quality of care and patient safety [1,2]
The affective state consists of emotional responses, including experience, expression, and physiology
This study focuses on the analysis of the individual’s affective state

Summary

Introduction

Effective disease management requires a constant search for factors relevant to improving the quality of care and patient safety [1,2]. The affective state consists of emotional responses, including experience, expression, and physiology. Those components aid in the interpretation or appraisal of the situation that provokes a given emotional response. It could be helpful in clinical practice to identify factors responsible for patient activation and engagement in the therapy [3]. Results of longitudinal studies suggest that patient activation predicts future health outcomes [4] and that it is possible to affect whether the patient successfully participates in the treatment and feels responsible for the healthcare process [5]. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of the changes in responding to a situation, making it possible to supplement the psychological data obtained through self-descriptive questionnaires with physiological ones [8,9,10]

Methods

Results

Discussion

Conclusion