Affective Computing and Loneliness: How This Approach Could Improve a Support System

Burzagli, Laura; Naldini, Simone

doi:10.1007/978-3-030-49108-6_35

Cited by 2 publications

(2 citation statements)

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“…Affective computing is defined by an interdisciplinary field of research that incorporates both sentiment analysis and emotion recognition (Poria et al, 2017). Scholars have posited the importance of affective computing to endow machines with the means to recognize, interpret or convey emotions and sentiments (Poria et al, 2017;Burzagli and Naldini, 2020). These capabilities allow the development and enhancement of personal care systems that interact better with humans, potentially improving a personal health and daily well-being (Burzagli and Naldini, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Scholars have posited the importance of affective computing to endow machines with the means to recognize, interpret or convey emotions and sentiments (Poria et al, 2017;Burzagli and Naldini, 2020). These capabilities allow the development and enhancement of personal care systems that interact better with humans, potentially improving a personal health and daily well-being (Burzagli and Naldini, 2020). Previous attempts in the development of affective computing have focused on emotion feature extraction and classification through human-robot interactions (Azuar et al, 2019;Rudovic et al, 2019;Yu and Tapus, 2019;Filippini et al, 2020;Rosula Reyes et al, 2020;Val-Calvo et al, 2020), facial expressions (Chronaki et al, 2015;rong Mao et al, 2015;Yang et al, 2018a;Zeng et al, 2018), and vocal responses (Wang et al, 2015;Fayek et al, 2017;Noroozi et al, 2017;Anuja and Sanjeev, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements

Branco

Ehteshami

Azgomi

et al. 2022

Front. Comput. Neurosci.

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Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.

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