Author_Institution :
Sch. of Electr. Eng., Tianjin Univ. of Technol., Tianjin, China
Abstract :
The exoskeleton robot technology is more and more used in the assisting stroke patients in implementing rehabilitation training. In this paper, a novel exoskeleton finger robot has been described to aim at helping varieties of hemiparalysis patients recover motor function. The robot system adopts the EEG control and mainly consists of exoskeleton finger robot, EEG system, HMI system, motor controllers unit, some sensors and a workstation. And the hand exoskeleton mechanism is portable, wearable and adjustable for patients doing home rehabilitation training. Base on the Denavit-Hartenberg (DH) parameters method, the kinematic model of finger has built to be used in designing the robot. Through the simulation software ADAMS (Automatic Dynamic Analysis of Mechanical Systems), the parameters of position, velocity and acceleration (PVA) in each joint are simulated. From the result, it can view that the robot has high movement ability to finish the Continuous Passive Motion (CPM). Besides, a comparison test is done to study whether there are some motion blocks in wearing exoskeleton robot. Form the curve figure, in the two situations, the angle range of the MCP (metacarpaophalangeal) joint is equal, which verifies the interference of robot is small. These experiments demonstrate the exoskeleton can provide high efficiency movement ability for stroke doing the rehabilitation. In the future, with the optimization design, the robot will improvement and has a bright application prospect in the rehabilitation field.
Keywords :
digital simulation; electroencephalography; handicapped aids; medical robotics; patient rehabilitation; robot kinematics; ADAMS simulation software; Denavit-Hartenberg parameter method; EEG control; EEG system; HMI system; MCP joint; automatic dynamic analysis of mechanical systems; continuous passive motion; exoskeleton finger rehabilitation robot; finger kinematic model; hand exoskeleton mechanism; hemiparalysis patients; kinematic analysis; metacarpaophalangeal joint; motor controller unit; position-velocity-acceleration parameters; rehabilitation training; stroke patients; Exoskeletons; Joints; Kinematics; Robot sensing systems; Thumb; Exoskeleton finger robot; Kinematic simulation analysis; Rehabilitation;