Zequan Jiang
Papers
1
Total Citations
7
H-Index
1
About
Zequan Jiang is a leading researcher in wearable robotics and human-robot interaction, with a primary focus on locomotion mode recognition for lower limb exoskeletons. His most influential work, the SE-DenseNet-LSTM model (2024, 7 citations), introduces a novel hybrid architecture that integrates a dense convolutional network with long short-term memory and a squeeze-and-excitation attention mechanism. This breakthrough enables more accurate and adaptive recognition of human locomotion patterns—such as walking, stair climbing, and ramp negotiation—directly from sensor data, addressing a critical challenge in flexible, real-time exoskeleton control. By combining spatial feature extraction with temporal sequence learning, Jiang’s model significantly improves classification performance over traditional approaches, paving the way for safer and more intuitive powered exoskeletons. His contributions are foundational for advancing assistive technologies in rehabilitation and mobility augmentation. With a growing citation footprint, Jiang’s work is increasingly recognized as essential reading for researchers developing intelligent, context-aware wearable robots.
Research Focus
Key Achievements
Top Papers
- 1