Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees

Abstract

In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (RMSE), and correlation coefficient (γ)) were calculated to verify the feasibility of the prediction method.

Study on the Control Method of Knee Joint Human–Exoskeleton Interactive System

“…The sEMG and joint angle regression model is established through deep belief networks and back propagation (BP) neural networks [ 18 ]. To further improve the prediction effect, sEMG, joint angles, and plantar pressure signals [ 19 , 20 ] are introduced into the generalized regression neural network for training and prediction. Similarly, sEMG and A-mode ultrasound [ 21 ] have been combined and introduced to build a vector machine regression model.…”