Next Article in Journal
Multi-Level Information Storage Using Engineered Electromechanical Resonances of Piezoelectric Wafers: A Concept Piezoelectric Quick Response (PQR) Code
Next Article in Special Issue
Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data
Previous Article in Journal
Feasibility of Backscatter Communication Using LoRAWAN Signals for Deep Implanted Devices and Wearable Applications
Previous Article in Special Issue
Writhing Movement Detection in Newborns on the Second and Third Day of Life Using Pose-Based Feature Machine Learning Classification
Article

Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures

1
Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland
2
Institute of Psychology, University of Silesia in Katowice, Bankowa 12, 40-007 Katowice, Poland
3
Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6343; https://doi.org/10.3390/s20216343
Received: 10 October 2020 / Revised: 2 November 2020 / Accepted: 4 November 2020 / Published: 6 November 2020
(This article belongs to the Special Issue Multimodal Sensing for Understanding Behavior and Personality)
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%). View Full-Text
Keywords: electrodermal activity; GSR; emotions analysis; psychological analysis; JAWS; clusterisation electrodermal activity; GSR; emotions analysis; psychological analysis; JAWS; clusterisation
Show Figures

Figure 1

MDPI and ACS Style

Romaniszyn-Kania, P.; Pollak, A.; Danch-Wierzchowska, M.; Kania, D.; Myśliwiec, A.P.; Piętka, E.; Mitas, A.W. Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures. Sensors 2020, 20, 6343. https://doi.org/10.3390/s20216343

AMA Style

Romaniszyn-Kania P, Pollak A, Danch-Wierzchowska M, Kania D, Myśliwiec AP, Piętka E, Mitas AW. Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures. Sensors. 2020; 20(21):6343. https://doi.org/10.3390/s20216343

Chicago/Turabian Style

Romaniszyn-Kania, Patrycja, Anita Pollak, Marta Danch-Wierzchowska, Damian Kania, Andrzej P. Myśliwiec, Ewa Piętka, and Andrzej W. Mitas. 2020. "Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures" Sensors 20, no. 21: 6343. https://doi.org/10.3390/s20216343

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop