ArticleSensors (Basel, Switzerland)2018
Automatic Emotion Perception Using Eye Movement Information for E-Healthcare Systems.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- ICTD: Combination of Improved CNN-Transformer and Enhanced Deep Canonical Correlation Analysis for Eye-Movement Emotion Classification.Brain sciences · 2026Article
- CBR-Net: A Multisensory Emotional Electroencephalography (EEG)-Based Personal Identification Model with Olfactory-Enhanced Video Stimulation.Bioengineering (Basel, Switzerland) · 2025Article
- Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications.Biosensors · 2022Article
- Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements.Biosensors · 2022Review
- Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges.Sensors (Basel, Switzerland) · 2020Review
- Human Emotion Recognition: Review of Sensors and Methods.Sensors (Basel, Switzerland) · 2020Review
- A Case Study of Facial Emotion Classification Using Affdex.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
3 authors.
Funding
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Abstract
Facing the adolescents and detecting their emotional state is vital for promoting rehabilitation therapy within an E-Healthcare system. Focusing on a novel approach for a sensor-based E-Healthcare system, we propose an eye movement information-based emotion perception algorithm by collecting and analyzing electrooculography (EOG) signals and eye movement video synchronously. Specifically, we extract the time-frequency eye movement features by firstly applying the short-time Fourier transform (STFT) to raw multi-channel EOG signals. Subsequently, in order to integrate time domain eye movement features (i.e., saccade duration, fixation duration, and pupil diameter), we investigate two feature fusion strategies: feature level fusion (FLF) and decision level fusion (DLF). Recognition experiments have been also performed according to three emotional states: positive, neutral, and negative. The average accuracies are 88.64% (the FLF method) and 88.35% (the DLF with maximal rule method), respectively. Experimental results reveal that eye movement information can effectively reflect the emotional state of the adolescences, which provides a promising tool to improve the performance of the E-Healthcare system.
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Registered trials
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