Papers
2
Total Citations
35
H-Index
2
About
Chien-Wen Chen is a pioneering researcher whose work bridges two seemingly distinct fields: affective computing and minimally invasive gynecologic surgery. In affective computing, Chen is best known for developing a deep hybrid neural network framework that recognizes human emotions from Galvanic Skin Response (GSR) signals—a breakthrough that captures the physiological signatures of emotional states. This highly cited 2020 work (26 citations) has laid important groundwork for wearable emotion-sensing technologies. Simultaneously, Chen has made significant contributions to robotic surgery, particularly in the transition from multiport to single-site robotic supracervical hysterectomy for benign gynecological diseases. A 2019 study (9 citations) from a single-institution experience established key selection criteria and demonstrated the feasibility of this less invasive approach. Chen’s dual expertise—applying deep learning to decode human physiology while advancing surgical robotics—exemplifies a rare interdisciplinary talent. By connecting computational methods with clinical practice, Chen’s work continues to impact both patient care and human-computer interaction research.
Research Focus
Key Achievements
Top Papers
- 1
- 2