Abstract In order to improve the accuracy of gait recognition, the gait recognition of human lower limb was studied based on the fusion of surface electromyography (sEMG), knee joint angle and plantar pressure. Firstly, the sEMG signals were decomposed by wavelet packet to extract the features of multi-scale energy and multi scale fuzzy entropy. Then, the principal component analysis (PCA) method was employed to reduce the dimension of the feature value of sEMG, and the feature vectors were constituted by the features of sEMG, plantar pressure and the knee energy. Finally, the feature vectors were inputted into the least squares support vector machine (PSO-LSSVM) optimized by the particle swarm to recognize gait of lower limb. The experimental results show that this method has higher recognition accuracy and validity than other methods.
［1］Lee S W, Yi T, Jung J W, et al. Design of a Gait Phase Recognition System That Can Cope With EMG Electrode Location Variation［J］.Automation Science and Engineering, 2015, 9(10): 1109-1019.
［2］Ahmed F, Paul P P, Gavrilova M L. DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect［J］. The Visual Computer, 2015,31(6): 1-10.
［3］Liu F, Wang Y, Wang Q, et al. A new gait recognition method using kinect via deterministic learning［C］//2016 12th World Congress on Intelligent Control and Automation (WCICA). Guilin,China,2016:830-835.
［4］高云园, 佘青山, 孟明,等. 基于多源信息融合的膝上假肢步态识别方法［J］. 仪器仪表学报, 2010,31(12): 2682-2688.
［5］席旭刚,朱海港,罗志增,等。 基于经验模态分解样本熵的肌电信号识别 ［J］. 计量学报, 2014,35(6):534-539.
［6］Zhou S. Sparse LSSVM in Primal Using Cholesky Factorization for Large-Scale Problems［J］. IEEE transactions on neural networks and learning systems, 2016, 27(4): 783-795.
［7］武昊, 席旭刚, 罗志增. 基于熵和PSO优化SVM的肌电信号跌倒识别［J］. 传感技术学报, 2015,(11):1586-1590.
［8］Tabrizi A, Garibaldi L, Fasana A, et al. Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine［J］. Meccanica, 2014, 50(3): 865-874.
［9］王保平, 刘升虎, 范九伦,等. 基于模糊熵的自适应图像多层次模糊增强算法［J］. 电子学报, 2005, 33(4):730-734.
［10］Bharti K K, Singh P K. Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering［J］. Expert Systems with Applications, 2015, 42(6): 3105-3114.
［11］Aydogdu M, Firat M. Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods［J］. Water Resources Management, 2015, 29(5): 1575-1590.
［12］杨景明,郭秋辰 ,孙浩,等. 基于改进果蝇算法与最小二乘支持向量机的轧制力预测算法研究 ［J］. 计量学报, 2016,37(5):505-508.
［13］Wang H B, Ma J H , Wang C D. Study on the classification of multi-spectral images based on a FSVM multi-class classifier with the binary tree［J］. Optoelectronics Letters, 2010, 6(1):61-64.
［14］朱喜华, 李颖晖, 李宁,等. 基于改进离散粒子群算法的传感器布局优化设计［J］. 电子学报, 2013, 41(10):2104-2108.
［15］Selakov A, Cvijetinovic′ D, Milovic′ L, et al. Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank［J］. Applied Soft Computing Journal, 2014, 16(3): 80-88.
［16］孟明, 佘青山, 罗志增. HMM在下肢表面肌电信号步态识别中的应用［J］. 华中科技大学学报(自然科学版), 2011, 39(S2):176-179.
［17］郭忠武, 丁海曙, 王广志,等. 基于运动学和动力学参数的步态识别研究［J］. 生物医学工程学杂志, 2005, 22(1):1-4.
2018, Vol. 39 
