ArticleSensors (Basel, Switzerland)2020
Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Emotion detection unveiled: A cognitive-computational synthesis of physiological models, machine learning, and datasets.Cognitive, affective & behavioral neuroscience · 2026Review
- Longitudinal observation of psychophysiological data as a novel approach to personalised postural defect rehabilitation.Scientific reports · 2025Article
- Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy.Sensors (Basel, Switzerland) · 2024Article
- EEG Dataset for the Recognition of Different Emotions Induced in Voice-User Interaction.Scientific data · 2024Article
- A linearized modeling framework for the frequency selectivity in neurons postsynaptic to vibration receptors.Cognitive neurodynamics · 2024Article
- Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition.PloS one · 2024Article
- Article
- ECG Multi-Emotion Recognition Based on Heart Rate Variability Signal Features Mining.Sensors (Basel, Switzerland) · 2023Article
- Affective Impressions Recognition under Different Colored Lights Based on Physiological Signals and Subjective Evaluation Method.Sensors (Basel, Switzerland) · 2023Article
- Emodiversity evaluation of remote workers through health monitoring based on intra-day emotion sampling.Frontiers in public health · 2023Observational
- Temporal variations in the pattern of breathing: techniques, sources, and applications to translational sciences.The journal of physiological sciences : JPS · 2022Review
- Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables.Scientific data · 2022Article
- Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis.PloS one · 2022Article
- EEG-based human emotion recognition using entropy as a feature extraction measure.Brain informatics · 2021Review
- Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Article
- Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli.Sensors (Basel, Switzerland) · 2021Article
- DRER: Deep Learning-Based Driver's Real Emotion Recognizer.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The
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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.