Ming Guo
Papers
1
Total Citations
22
H-Index
1
About
Ming Guo is a leading researcher in affective computing and human–computer interaction, with a focus on emotion recognition through sensor-based motion analysis. His most cited work, "Attention‐based sensor fusion for emotion recognition from human motion by combining convolutional neural network and weighted kernel support vector machine and using inertial measurement unit signals" (2023, 22 citations), introduces a novel hybrid deep learning framework that integrates attention mechanisms with CNN and SVM classifiers to decode emotional states from inertial measurement unit (IMU) signals. This contribution addresses a critical gap in non-invasive, wearable emotion sensing, with direct applications in medical rehabilitation, mental health monitoring, and adaptive human–robot interaction. By fusing multiple sensor modalities and optimizing feature extraction, Guo’s approach achieves robust performance even in complex, real-world scenarios. His work exemplifies the convergence of machine learning, signal processing, and psychology, offering scalable solutions for machines to interpret human affect. With growing citation impact, Ming Guo is establishing himself as an innovator at the intersection of sensor technology and emotional AI, paving the way for more empathetic and responsive interactive systems.
Research Focus
Key Achievements
Top Papers
- 1