Detection Of Lower Limb Posture And Research On Prediction Algorithm For Movement State

In recent years, intelligent lower limb prosthesis is an attentive research project in the fields of robotics and biomedical engineering, the detection of lower limb's posture and prediction of motion state is the basis for control of intelligent lower limb prosthesis. To study intelligent prosthesis provides amputees with prosthesis which have good performance to improve their life quality, and has a great significance for constructing harmonious society. Intelligent prosthesis with high capability and bionic degree has been realized overseas, but the domestic research in the field still has a large gap with their study, there is no mature intelligent artificial leg product. The exploration and development of intelligent artificial leg is of great significance to shorten the gap with the developed countries and to promote Rehabilitation Medicine Engineering Technology and prosthesis industry of China.Combining some research tasks of"the research on the key technology of adaptive intelligent knee prosthesis"(the national 863 Project 2008AA04Z212), this paper aim at the disabilities which have only one knee lower limb amputation to use the intelligent prostheses, obtain multi-source motion information of the lower limb posture, adopt effective prediction algorithms to predict the moving state of lower limb, lay the foundation for the study of Adaptive Motion Control. The major research works are as follows:(1) For the detection of lower limb posture, a set of system for collection of lower limb's multi-source motion information was built, which consisted of some sensors and the measurement device of plantar pressure, knee angle and EMG signal. The detailed method was as follows: The shoe attached PVDF force sensor was used to obtain heel and toe regional pressure; two ADXL203 accelerometers was combined to obtain the angle of knee joint; the MyoTrace 400 was used to obtain EMG signals of the Vastus medialis muscle, the semitendinosus muscle, the long adductor muscle and the tensor fascia lata.(2) According to the law of lower limb movement's gait cycle, a method combining gait and phase for mode subdivision of lower limb movement was given, which met the need of motion control of the intelligent artificial leg. After researching and analyzing the characteristics of plantar pressure, knee angle and EMG three signals, the corresponding effective feature extraction methods were proposed in this paper: the feature extraction based on correlation analysis was used in EMG; the method of judging based on threshold was used in plantar pressure signal ; in order to reduce the workload of processing the knee angle signal, the mean of the knee angles under the same gait phase was used as the feature of the angle signal of knee joint.(3) The CPN neural network was used to identify the moving state of lower limb. The input of the network was a concatenation feature vector, consisted of the features of EMG signal, plantar pressure and the knee angle signal. Then the results of identification through the CPN network and the methods given by Task Force members were compared and analyzed.(4) The prediction algorithms of Radial Basis Function neural network(RBF) and Adaptive Neural Fuzzy Inference System(ANFIS)were studied. On the basis of comparison and analysis of the forecasting results of knee joint angle changes through the two methods, the prediction of the lower limb moving state based on the multi-source motion information was performed by using ANFIS. The experimental results showed that the moving state can be predicted effectively by using ANFIS and the average prediction error met the need of the prediction accuracy for the lower limb moving state, the foundation for the establishment of the lower limb motion's adaptive prediction control system was laid.The major innovation is as follow:The prediction system for the lower limb moving state based on the multi-source motion information was established, the idea was like this: the concatenation feature vector consisted of the features of EMG signal, planter pressure and the knee joint's angle signal was input to the CPN neural network to identify the current motion state of lower limb, Combined with the first three parameters of motion state to predict future motion state of lower limb by using Adaptive Fuzzy Neural Inference System(ANFIS).