Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures

Abstract

Nowadays, the dynamic development of technology allows for the design of systems based on various information sources and their integration into hybrid expert systems. One of the areas of research where such systems are especially helpful is emotion analysis. The sympathetic nervous system controls emotions, while its function is directly reflected by the electrodermal activity (EDA) signal. The presented study aimed to develop a tool and propose a physiological data set to complement the psychological data. The study group consisted of 41 students aged from 19 to 26 years. The presented research protocol was based on the acquisition of the electrodermal activity signal using the Empatica E4 device during three exercises performed in a prototype Disc4Spine system and using the psychological research methods. Different methods (hierarchical and non-hierarchical) of subsequent data clustering and optimisation in the context of emotions experienced were analysed. The best results were obtained for the k-means classifier during Exercise 3 (80.49%) and for the combination of the EDA signal with negative emotions (80.48%). A comparison of accuracy of the k-means classification with the independent division made by a psychologist revealed again the best results for negative emotions (78.05%).

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

“…Based on the available literature [ 9 , 10 , 34 , 35 , 81 ], for each part of the EDA, the same statistical parameters as for the heart signals were computed. Furthermore, the following coefficients were also computed from the EDA signal: total energy, mean and median energy of the signal, entropy, coefficient of the slope of the regression line allowing to determine the trend—tonicity of signal, coefficients of regression line shift, a distance of values of consecutive EDA samples from the regression line, number of signal intersections with the regression line and the quadratic metric of the discrepancy between predicted and observed data (obj).…”

“…It offers the opportunity to support behavioural diagnostic and analyze the subjects’ physiological reactions in various everyday situations more thoroughly. The approach based on the EDA signal analysis and its features is an example of such attempts [ 10 ]. To objectivize the patient’s emotional state during therapy, a set of analytical parameters—signal errors was proposed, based on which the affect classification accuracy reached the level of 86.37%.…”

“…The previous results indicate the significance of neuroticism and negative emotions for intensifying stress experience during physiotherapeutic procedures in a non-clinical group [ 9 , 10 ]. Anxiety and stress are interrelated and condition one another [ 20 , 21 , 22 ].…”

“…Emotions during healthcare are mainly triggered by invasive or frightening treatment experiences and pain during surgical procedures [ 6 , 7 ]. 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 ].…”

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

“…Clinical studies have shown that such meditation is very effective in reducing stress [ 33 ]. Last but not least, Withings Scanwatch [ 196 ] with medical grade ECG and oximeter and two devices from company Empatica: Embrace 2 and E4 should be mentioned [ 197 , 198 , 199 ]. Both are in the form of wristband and can stream the following variables in real time: EDA, wrist temperature and accelerometric signal.…”