Papers
8
Total Citations
290
H-Index
5
About
Qingling Li’s research lies at the intersection of rehabilitation robotics, human motion analysis, and autonomous robot navigation, with a particular focus on using surface electromyography (sEMG) signals for intuitive human-robot interaction. Her most influential work, a 2011 paper on using BP neural networks to continuously estimate human leg joint angles from sEMG, has garnered 199 citations, establishing a foundational method for controlling lower-limb assistive devices. She has made significant contributions to both upper and lower limb rehabilitation, including a 2006 paper on a 5-DOF wearable robot that enables hemiplegic patients to use their healthy limb to guide rehabilitation exercises. In signal processing, Li proposed a novel discrete wavelet transform feature for sEMG-based upper limb motion recognition, advancing pattern recognition for prosthetic and rehabilitative control. Beyond biomedical applications, she has also contributed to mobile robotics, developing an improved unscented particle filter (IUPF) algorithm for robust Monte Carlo localization in unknown environments. Her work on adaptive RBF neural network control for lower limb rehabilitation robots further demonstrates her commitment to creating intelligent, responsive systems that adapt to individual patient needs.
Research Focus
Key Achievements
Top Papers
- 1
- 2Neural network based FastSLAM for autonomous robots in unknown environments34 citations · 2015
- 3sEMG Based Control for 5 DOF Upper Limb Rehabilitation Robot System20 citations · 2006
- 4SEMG feature extraction methods for pattern recognition of upper limbs17 citations · 2011
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- 7
- 8Dynamic Analysis and Simulation of 6-DOF Industrial Robot2 citations · 2008