Preliminary validation of a measurement system for emotion recognition

Apicella, Andrea; Arpaïa, Pasquale; Mastrati, Giovanna; Moccaldi, Nicola; Prevete, Roberto

doi:10.1109/memea49120.2020.9137353

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…There were also a lot of different approaches to emotion assessment based on data other than physiological signals from wearable devices. Several methods used neuroimaging combined with machine learning algorithms to objectivize psychological diagnostics of patients [ 61 , 62 ] and search for the biomarkers that identify the particular emotional state [ 63 ]. Another approach was for systems to recognise emotions based on facial expression [ 64 , 65 , 66 , 67 ] or facial micromovements [ 67 ].…”

Section: Introductionmentioning

confidence: 99%

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

Romaniszyn-Kania

Pollak

Bugdoł

et al. 2021

Sensors

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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%.

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