About
I am a PhD student in hand lab with Dr. Derek Kamper at RIC, I also work on my dissertation project in Nicho's Lab at University of Chicago.
My research focuses on brain- machine interface system, I am interested in both robotics side and neural motor control side. I am from electrical engineering background, and get trained on human physiology and neural biology at VCU medical school and University of Chicago. We design, build, program various wearable robots ourselves in the lab.
Research Work
Cortical control of hand exoskeleton for grasping
In natural grasping, humans are highly reliant on the exquisite sensory information from the hand to properly perform tasks. This is lost with use of a robot, even though many individuals with motor hand impairment might have substantial sparing of sensory information. We propose to utilize this rich sensory feedback by having the individual’s own hand make contact and manipulate the object through the use of a hand exoskeleton that is controlled via populations of cortical neurons. The individual will be fully integrated into the activity, with the exoskeleton providing actuation and the individual’s own hand providing sensation.
Kinematics Control of Activated Thumb Exoskeleton
We built the actuated thumb exoskeleton (ATX) to facilitate research in examining motor control and hand rehabilitation. The ATX presented in this work aims to provide independent bi-directional actuation in each of the 5 degrees-of-freedom (DOF) of the thumb using a novel flexible shaft based mechanism that has 5 active DOF and 3 passive DOF.
Ref: Wang F, Shastri M, Jones CL, Qian K, Kamper DG , Sarkar N. (2016). Design and Development of a Biomimetic Exoskeleton for Examining Motor Control in the Stroke Thumb ". Advanced Robotics, 30(3) 165-177.
FingerBot for joint stiffness evaluation
Early research work on EEG motor intention detection
To develop a practical motor imagery-based brain-controlled switch as functional as a real-world switch that is reliable with a minimal false positive operation rate and convenient for users without the need of attention to the switch during a ‘No Control’ state (when not to activate the switch).
Ref: Qian, K., Nikolov, P., Huang, D., Fei, D. Y., Chen, X., & Bai, O. (2010). A motor imagery-based online interactive brain-controlled switch: Paradigm development and preliminary test. Clinical neurophysiology, 121(8), 1304-1313.
Side Projects
Neural Network Controller for Robotic Manipulator
In deriving adaptive controller, it requires substantial work to compute the Y matrix, such that,
System performance depends on the accuracy of regression matrix Y and the knowledge of complete system dynamics. However the estimation of the adaptive controller reminded me the function approximation ability of neural network. It can give great estimation only by training on input and output. If a neural network can replace the role of Y matrix, then good system performance will be achieved as adaptive controller without preliminary dynamics analysis to compute Y.
Ref: Class Project Paper [PDF]
Mechanical properties vary for different regions of the finger extensor apparatus
The extensor apparatus, an aponeurosis that covers the dorsal side of each finger, transmits force from a number of musculotendons to the phalanges. Multiple tendons integrate directly into the structure at different sites and the extensor apparatus attaches to the phalanges at multiple points. The variations in mechanical properties within the hood may impact prediction of force transmission and, thus, should be considered when modeling the action of the extensor apparatus.
Qian, K., Traylor, K., Lee, S. W., Ellis, B., Weiss, J., & Kamper, D. (2014). Mechanical properties vary for different regions of the finger extensor apparatus. Journal of biomechanics, 47(12), 3094-3099.
