A5032924019- EEG and Brain-Computer Interfaces
- Emotion and Mood Recognition
- Nutrition and Health in Aging
- Stroke Rehabilitation and Recovery
- Cerebral Palsy and Movement Disorders
- ECG Monitoring and Analysis
- Non-Invasive Vital Sign Monitoring
Sookmyung Women's University
2022-2024
Emotion recognition from electroencephalogram (EEG) signals requires accurate and efficient signal processing feature extraction. Deep learning technology has enabled the automatic extraction of raw EEG features that contribute to classifying emotions more accurately. Despite such advances, classification signals, especially recorded during recalling specific memories or imagining emotional situations not yet been investigated. In addition, high-density using deep neural networks faces...
Building a robust facial expression recognition (FER) system remains challenging problem due to the emotional ambiguity of expressions. Recent approaches employ both expressions and physiological signals design multi-modal emotion systems. However, these require physical contact with skin as they need use sensor modalities. To meet demands for non-contact system, we convolutional recurrent neural network (CRNN) extract features utilize estimating heart rate (HR) from face image sequences. In...
Sarcopenia, a condition characterized by muscle weakness and mass loss, poses significant risks of accidents complications. Traditional diagnostic methods often rely on physical function measurements like handgrip strength which can be challenging for affected patients, including those with stroke. To address these challenges, we propose novel sarcopenia diagnosis model utilizing stimulated contraction signals captured via wearable devices. Our approach achieved impressive results, an...