Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm

• Published in: Symmetry (Basel) Vol. 12; no. 1; p. 130
• Main Authors: Deng, Yanxia, Gao, Farong, Chen, Huihui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2020
• Subjects: Accuracy; Algorithms; Artificial neural networks; Back propagation networks; Correlation coefficients; Datasets; Eigenvalues; Feature extraction; Gait; Human motion; Joints (anatomy); Knee; Machine learning; Motion capture; Motion simulation; Neural networks; Parameter identification; Principal components analysis; Rehabilitation; Root-mean-square errors; Sports medicine; Support vector machines; Three dimensional motion; Training
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym12010130

Surface electromyogram (sEMG) signals are easy to record and offer valuable motion information, such as symmetric and periodic motion in human gait. Due to these characteristics, sEMG is widely used in human-computer interaction, clinical diagnosis and rehabilitation medicine, sports medicine and other fields. This paper aims to improve the estimation accuracy and real-time performance, in the case of the knee joint angle in the lower limb, using a sEMG signal, in a proposed estimation algorithm of the continuous motion, based on the principal component analysis (PCA) and the regularized extreme learning machine (RELM). First, the sEMG signals, collected during the lower limb motion, are preprocessed, while feature samples are extracted from the acquired and preconditioned sEMG signals. Next, the feature samples dimensions are reduced by the PCA, as well as the knee joint angle system is measured by the three-dimensional motion capture system, are followed by the normalization of the feature variable value. The normalized sEMG feature is used as the input layer, in the RELM model, while the joint angle is used as the output layer. After training, the RELM model estimates the knee joint angle of the lower limbs, while it uses the root mean square error (RMSE), Pearson correlation coefficient and model training time as key performance indicators (KPIs), to be further discussed. The RELM, the traditional BP neural network and the support vector machine (SVM) estimation results are compared. The conclusions prove that the RELM method, not only has ensured the validity of results, but also has greatly reduced the learning train time. The presented work is a valuable point of reference for further study of the motion estimation in lower limb.