ArticleSensors (Basel, Switzerland)2022
Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Prediction-based attention computing: a proof of concept study.Virtual reality · 2026Article
- The cognitive impacts of large language model interactions on problem solving and decision making using EEG analysis.Frontiers in computational neuroscience · 2025Article
- Article
- Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study.Journal of medical Internet research · 2023Article
- Assessing Feasibility of Cognitive Impairment Testing Using Social Robotic Technology Augmented with Affective Computing and Emotional State Detection Systems.Biomimetics (Basel, Switzerland) · 2023Article
- Special Issue "Feature Papers in Biosensors Section 2022".Sensors (Basel, Switzerland) · 2023Article
- With or without the Mask: Age Differences in Perceived Trustworthiness and Health during the COVID-19 Pandemic.Behavioral sciences (Basel, Switzerland) · 2023Article
- Enabling Remote Responder Bio-Signal Monitoring in a Cooperative Human-Robot Architecture for Search and Rescue.Sensors (Basel, Switzerland) · 2022Article
- Psychophysiological Assessment of Children with Cerebral Palsy during Robotic-Assisted Gait Training through Infrared Imaging.International journal of environmental research and public health · 2022Article
- Automated Emotion Identification Using Fourier-Bessel Domain-Based Entropies.Entropy (Basel, Switzerland) · 2022Article
- Extended reality interventions for health and procedural anxiety: An overview of reviews.Digital healthReview
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect's four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.