Abstract: The aim of this paper is to present the kinematic
analysis and mechanism design of an assistive robotic leg for
hemiplegic and hemiparetic patients. In this work, the priority is to
design and develop the lightweight, effective and single driver
mechanism on the basis of experimental hip and knee angles- data for
walking speed of 1 km/h. A mechanism of cam-follower with three
links is suggested for this purpose. The kinematic analysis is carried
out and analysed using commercialized MATLAB software based on
the prototype-s links sizes and kinematic relationships. In order to
verify the kinematic analysis of the prototype, kinematic analysis data
are compared with the experimental data. A good agreement between
them proves that the anthropomorphic design of the lower extremity
exoskeleton follows the human walking gait.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification