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Low-invasive Neurophysiological Evaluation of Human Emotional State on Teleworkers

Topics: Computational intelligence applications in working environments ; ICT tools for modelling and monitoring worker state (functional abilities, cognitive capacity, mental fatigue, workload, etc.), activity, behaviour and emotional status ; Unobtrusive and pervasive health monitoring at the workplace

Authors: Vincenzo Ronca 1 ; 2 ; Gianluca Di Flumeri 2 ; 3 ; Andrea Giorgi 2 ; Alessia Vozzi 1 ; 2 ; Pietro Aricò 2 ; 3 ; Nicolina Sciaraffa 2 ; 3 ; Luca Tamborra 1 ; 2 ; 4 ; Ilaria Simonetti 1 ; 2 ; 4 ; Antonello Di Florio 2 ; Fabio Babiloni 2 ; 3 ; 5 and Gianluca Borghini 2 ; 3

Affiliations: 1 Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University, Rome 00185, Italy ; 2 BrainSigns srl, Rome 00185, Italy ; 3 Department of Molecular Medicine, Sapienza University of Rome, Rome 00185, Italy ; 4 People Advisory Services Department, Ernst & Young, Rome 00187, Italy ; 5 Department of Computer Science, Hangzhou Dianzi University, Hangzhou, China

Keyword(s): Facial Video, Neurophysiological Assessment, Signal Processing, Heart Rate, Electrodermal Activity, Emotional State Evaluation.

Abstract: Human emotions decoding and assessment is a hot research topic since its implications would be relevant in a huge set of clinical and social applications. Current emotion recognition and evaluation approaches are usually based on interactions between a “patient” and a “specialist”. However, this methodology is intrinsically affected by subjective biases and lack of objectiveness. Recent advancements in neuroscience enable the use of traditional biosensors and maybe commercial wearable devices, which lead to a certain grade of invasiveness for the subject. The proposed study explored an innovative low-invasive hybrid method, based on the use of video data and smart bracelet, to overcome such technological limitations. In particular, we investigated the capability of an Emotional Index (EI), computed by combining the Heart Rate (HR) and the Skin Conductance Level (SCL) estimated through video-based and wearable technology, in discriminating Positive and Negative emotional state during interactive webcalls. The results revealed that the computed EI significantly increased during the Positive condition compared to the Negative one (p = 0.0008) and the Baseline (p = 0.003). Such evidences were confirmed by the subjective data and the classification performance parameters. In this regard, the EI discriminated between two emotional states with an accuracy of 79.4%. (More)

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Paper citation in several formats:
Ronca, V.; Di Flumeri, G.; Giorgi, A.; Vozzi, A.; Aricò, P.; Sciaraffa, N.; Tamborra, L.; Simonetti, I.; Di Florio, A.; Babiloni, F. and Borghini, G. (2021). Low-invasive Neurophysiological Evaluation of Human Emotional State on Teleworkers. In Proceedings of the 13th International Joint Conference on Computational Intelligence - SmartWork; ISBN 978-989-758-534-0; ISSN 2184-3236, SciTePress, pages 427-434. DOI: 10.5220/0010726700003063

@conference{smartwork21,
author={Vincenzo Ronca. and Gianluca {Di Flumeri}. and Andrea Giorgi. and Alessia Vozzi. and Pietro Aricò. and Nicolina Sciaraffa. and Luca Tamborra. and Ilaria Simonetti. and Antonello {Di Florio}. and Fabio Babiloni. and Gianluca Borghini.},
title={Low-invasive Neurophysiological Evaluation of Human Emotional State on Teleworkers},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence - SmartWork},
year={2021},
pages={427-434},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010726700003063},
isbn={978-989-758-534-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence - SmartWork
TI - Low-invasive Neurophysiological Evaluation of Human Emotional State on Teleworkers
SN - 978-989-758-534-0
IS - 2184-3236
AU - Ronca, V.
AU - Di Flumeri, G.
AU - Giorgi, A.
AU - Vozzi, A.
AU - Aricò, P.
AU - Sciaraffa, N.
AU - Tamborra, L.
AU - Simonetti, I.
AU - Di Florio, A.
AU - Babiloni, F.
AU - Borghini, G.
PY - 2021
SP - 427
EP - 434
DO - 10.5220/0010726700003063
PB - SciTePress