This paper puts forward an information acquisition and control system for the exoskeleton robot, which can collect movement and location information of the robot timely through a variety of sensors. The information is preprocessed by the microcontroller firstly and then transmitted to the host computer for data analysis and processing by ZigBee wireless transmission module to analyze the movement intention of human by virtue of the monitoring software on the host computer. To achieve assistance, the motor drive will be controlled by the robot through CAN bus, and the robot can effectively analyze human’s intention and monitor the operation status of the assisted robot in practical applications, finally enhancing the body's walking ability.

[abstFig src='/00280003/18.jpg' width=""300"" text='The result of parameters optimization by GA' ] The support vector machine (SVM) we propose for automated gait and posture recognition is based on acceleration. Acceleration data are obtained from four accelerators attached to the human thigh and lower leg. In the experiment, volunteers take part in four gaits and postures, i.e., sitting, standing, walking and ascending stairs. Acceleration data that are preprocessed include normalization, a wavelet filter and dimension reduction. We used the SVM and a neural network to analyze the data processed. Simulation results indicate that SVM parametersCandgselected by a genetic algorithm (GA) are more effective for gait and posture analysis when compared to the parameterCandgselected by a grid search. The overall classification precision of the four gaits and postures exceeds 90.0%, and neural network simulation results indicate that the SVM using the GA is preferable for use in analysis.

In order to get a higher recognition accuracy of gaits, a gait recognition system based on SVM and acceleration is proposed in this paper. The acceleration data are obtained from acceleration acquisition system based on AT90CAN128 and four accelerometers that attached on human's thigh and shank. Acceleration data includes four gaits which consist of sitting, standing, walking and going upstairs. After normalization and median filtering are used for data, GA based on SVM is applied for gait recognition. The overall recognition accuracy of four gaits is more than 90%. Proved by the results of experiments, gait recognition based on acceleration and SVM whose parameter C and g selected by GA is an effective approach. This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unres distribution, and reproduction in any medium, provided the original work is properly cited.