An intention driven hand functions task training robotic system

A novel design of a hand functions task training robotic system was developed for the stroke rehabilitation. It detects the intention of hand opening or hand closing from the stroke person using the electromyography (EMG) signals measured from the hemiplegic side. This training system consists of an embedded controller and a robotic hand module. Each hand robot has 5 individual finger assemblies capable to drive 2 degrees of freedom (DOFs) of each finger at the same time. Powered by the linear actuator, the finger assembly achieves 55 degree range of motion (ROM) at the metacarpophalangeal (MCP) joint and 65 degree range of motion (ROM) at the proximal interphalangeal (PIP) joint. Each finger assembly can also be adjusted to fit for different finger length. With this task training system, stroke subject can open and close their impaired hand using their own intention to carry out some of the daily living tasks.

[1]  T. Milner,et al.  HandCARE: A Cable-Actuated Rehabilitation System to Train Hand Function After Stroke , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  E. Taub,et al.  The learned nonuse phenomenon: implications for rehabilitation. , 2006, Europa medicophysica.

[3]  Etienne Burdet,et al.  Rehabilitation of grasping and forearm pronation/supination with the Haptic Knob , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[4]  M. Chen,et al.  Interactive rehabilitation robot for hand function training , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[5]  L. Der-Yeghiaian,et al.  Robot-based hand motor therapy after stroke. , 2007, Brain : a journal of neurology.

[6]  H.I. Krebs,et al.  Design, Characterization, and Impedance Limits of a Hand Robot , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[7]  M. Hallett,et al.  The role of the human motor cortex in the control of complex and simple finger movement sequences. , 1998, Brain : a journal of neurology.

[8]  Grigore C. Burdea,et al.  The Rutgers Master II-new design force-feedback glove , 2002 .

[9]  H. Kawasaki,et al.  Development of a Hand Motion Assist Robot for Rehabilitation Therapy by Patient Self-Motion Control , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[10]  Yasuhisa Hasegawa,et al.  Wearable handling support system for paralyzed patient , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.