Affiliations: Biomechatronic Learning Lab, Department of Electrical and Microelectronic Engineering, Kate Gleason College of Engineering, Rochester Institute of Technology, Rochester, NY, USA
Abstract: This work examines two different types of myoelectric control schemes for the purpose of rehabilitation robot applications. The first is a commonly used technique based on a Gaussian classifier. It is implemented in real time for healthy subjects in addition to a subject with Central Cord Syndrome (CCS). The myoelectric control scheme is used to control three degrees of freedom (DOF) on a robot manipulator which corresponded to the robot's elbow joint, wrist joint, and gripper. The classes of motion controlled include elbow flexion and extension, wrist pronation and supination, hand grasping and releasing, and rest. Healthy subjects were able to achieve 90% accuracy. Single DOF controllers were first tested on the subject with CCS and he achieved 100%, 96%, and 85% accuracy for the elbow, gripper, and wrist controllers respectively. Secondly, he was able to control the three DOF controller at 68% accuracy. The potential applications for this scheme are rehabilitation and teleoperation. To overcome limitations in the pattern recognition based scheme, a second myoelectric control scheme is also presented which is trained using electromyographic (EMG) data derived from natural reaching motions in the sagittal plane. This second scheme is based on a time delayed neural network (TDNN) which has the ability to control multiple DOF at once. The controller tracked a subject's elbow and shoulder joints in the sagittal plane. Results showed an average error of 19° for the two joints. This myoelectric control scheme has the potential of being used in the development of exoskeleton and orthotic rehabilitation applications.
