Xiling Xiao
Papers
4
Total Citations
19
H-Index
2
About
Xiling Xiao is a leading researcher in assistive robotics and human–robot interaction, with a focus on developing intelligent wearable systems for rehabilitation and mobility support. Her work centers on grasp intention recognition, supernumerary robotic limbs, and coordination control for individuals with motor impairments, including stroke patients and the elderly. Xiao’s key contributions include a gaze and environmental context-guided deep neural network for grasp intention recognition (11 citations), which enables more intuitive control of assistive robots. She also designed a flexible wearable supernumerary robotic limb for chronic stroke patients (4 citations), inspired by bending pneumatic muscles and elephant trunk mechanics, to aid finger rehabilitation and grasping. Her research extends to human–robot coordination control for sit-to-stand assistance using supernumerary robotic legs (2 citations), addressing fall risks in hemiparetic patients. Additionally, Xiao developed an online monitoring method based on Karush–Kuhn–Tucker optimized zonotope set-membership filtering (2 citations) to enhance safety during sit-to-stand movements. Her innovative, user-centered designs are advancing the field of wearable robotics, offering practical solutions for aging populations and individuals with limited mobility.
Research Focus
Key Achievements
Top Papers
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