Abstract

The detection of emotions is fundamental in many areas related to health and well-being. This paper presents the identification of the level of arousal in older people by monitoring their electrodermal activity (EDA) through a commercial device. The objective was to recognize arousal changes to create future therapies that help them to improve their mood, contributing to reduce possible situations of depression and anxiety. To this end, some elderly people in the region of Murcia were exposed to listening to various musical genres (flamenco, Spanish folklore, Cuban genre and rock/jazz) that they heard in their youth. Using methods based on the process of deconvolution of the EDA signal, two different studies were carried out. The first, of a purely statistical nature, was based on the search for statistically significant differences for a series of temporal, morphological, statistical and frequency features of the processed signals. It was found that Flamenco and Spanish Folklore presented the highest number of statistically significant parameters. In the second study, a wide range of classifiers was used to analyze the possible correlations between the detection of the EDA-based arousal level compared to the participants’ responses to the level of arousal subjectively felt. In this case, it was obtained that the best classifiers are support vector machines, with 87% accuracy for flamenco and 83.1% for Spanish Folklore, followed by K-nearest neighbors with 81.4% and 81.5% for Flamenco and Spanish Folklore again. These results reinforce the notion of familiarity with a musical genre on emotional induction.

Highlights

  • Understanding and recognizing human emotions has been identified as a main interest area in smart systems [1,2,3,4,5]

  • One study analyzed some Electrodermal activity (EDA) features only, and the second, based on classifiers, examined possible correlations between the objective detection of the arousal level from processed physiological EDA signals and the level of arousal subjectively perceived by participants when answering the self-assessment manikins (SAM) questionnaire

  • We have presented a solution for the detection of the level of arousal from electrodermal signals (EDA) in people through their exposure to musical stimuli

Read more

Summary

Introduction

Understanding and recognizing human emotions has been identified as a main interest area in smart systems [1,2,3,4,5]. Such systems are being applied in many fields like well-being and healthcare [6,7,8,9,10,11], safe driving [12], smart cities [13] and smart environments [14,15], among others. Arousal and dominance are three independent emotional dimensions to describe people’s state of feeling [16,17]. Fluctuations in arousal are regulated by the autonomic nervous system, which is mainly controlled by the balanced activity of the parasympathetic and sympathetic systems [20]. In their studies with volunteers the participants’ feelings have been obtained by questionnaires in the form of Likert scales, self-assessment manikins (SAM) and free text [23,24,25,26]

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call