[1] 杨鹏,刘作军,耿艳利, 等. 智能下肢假肢关键技术研究进展[J]. 河北工业大学学报,2013,42(1):76-80.
YANG Peng, LIU Zuo-jun, GENG Yan-li, et al. Research advance on key technology of intelligent lower limb prosthesis [J]. Journal of Hebei University of Technology, 2013, 42(1): 76-80.
[2] FRANK S, HUSEYIN A V, MICHAEL G. Upslope walking with a powered knee and ankle prosthesis: initial results with an amputee subject [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2011, 19(1): 71-79.
[3] ABEL E W, ZACHARIA P C, FORSTER A, et al. Neural network analysis of the EMG interference pattern [J]. Medical Engineer and Physics,1996, 18(1): 12-17.
[4] 佘青山,孟 明,罗志增,等. 基于多核学习的下肢肌电信号动作识别[J]. 浙江大学学报:工学版,2010,44(7):1292-1297.
SHE Qing-shan, MENG Ming, LUO Zhi-zeng, et al. Electromyography movement recognition of lower limb based on multiple kernel learning[J]. Journal of Zhejiang University: Engineering Science, 2010, 44(7): 1292-1297.
[5] 吴剑锋,吴 群,孙守迁,等. 简约支持向量机分类算法在下肢动作识别中的应用研究[J]. 中国机械工程,2011, 22(4):433-438.
WU Jian-feng, WU Qun, SUN Shou-qian. Research on classification algorithm of reduced support vector machine for low limb movement recognition[J]. Chinese Journal of Mechanical, 2011, 22(4): 433-438.
[6] HE H, TODD A K, ROBERT D L. A strategy for identifying locomotion modes using surface electromyography [J]. IEEE Transactions on Biomedical Engineering, 2009, 56(1): 65-73.
[7] DU L, ZHANG F, LIU M, et al.Toward design of an environment-aware adaptive locomotion-mode-recognition system [J]. IEEE Transactions on Biomedical Engineering, 2012, 59(10): 2716-2726.
[8] STOLZE H, KUHTZ-BUSCHBECK J P, MONDWURF C, et al. Retest reliability of spatiotemporal gait parameters in children and adults [J]. Gait and Posture,1998, 7(2):125-130.
[9] MILICA D. Automatic recognition of gait phases from accelerations of leg segments[C] ∥ 9th Symposium on Neural Network Applications in Electrical Engineering. Belgrade: [s.n.], 2008: 121-124.
[10] LAU H, TONG K. The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot [J]. Gait and Posture, 2008, 27(2): 248-257.
[11] OSCAR D, ALFREDO J, MIGUEL A, et al. Centinela: a human activity recognition system based on acceleration and vital sign data[J]. Pervasive and Mobile Computing, 2012, 8(5): 717-729.
[12] 李祚泳,汪嘉杨,郭淳. PSO算法优化BP网络的新方法及仿真实验[J]. 电子学报,2008,36(11):2224-2228.
LI Zuo-yong, WANG Jia-yang, GUO Chun. A new method of BP network optimized based on particle swarm optimization and simulation test [J]. Acta Electronica Sinica, 2008, 36(11): 2224-2228.
[13] HA K H, VAROL H A, GOLDFARB M. Volitional control of a prosthetic knee using surface electromyography[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(1): 144-151.
[14] 王喜太,王强,张晓玉,等. 基于肌电传感器的下肢残肢康复训练模式识别的研究[J]. 中国康复理论与实践,2009,15(1):90-92.
WANG Xi-tai, WANG Qiang, ZHANG Xiao-yu, et al. Pattern recognition in rehabilitative exercises of lower residual limbs based on electromyography sensor[J]. Chinese Journal of Rehabilitation Theory and Practise, 2009, 15(1): 90-92.
[15] 鲍必赛,楼晓俊,李隽颖. 主成分分析在震动信号目标识别算法中的应用[J]. 华中科技大学学报:自然科学版,2012,40(7):24-28.
BAO Bi-sai, LOU Xiao-jun, LI Jun-ying. Application of principal component analysis in target recognition algorithm of seismic signals[J]. Joarnal of Huazhong University of Science and Technology: Natural Science Edition, 2012, 40(7): 24-28.
[16] 董九英. 多传感器数据融合的主成分方法研究[J]. 计算机工程与应用,2009,45(33):111-113.
DONG Jiu-ying. Study on principle component method for multi-sensor data fusion[J]. Computer Engineering and Applications, 2009, 45(33): 111-113.
[17] 石欣,雷璐宁,熊庆宇. 基于二次特征提取与 SVM 的异常步态识别[J]. 仪器仪表学报,2011,32(3):673-677.
SHI Xin, LEI Lu-ning, XIONG Qing-yu. Abnormal gait recognition based on quadratic feature extraction and support vector machine [J]. Chinese Journal of Scientific Instrument, 2011, 32(3): 673-677.
[18] 罗勇,和小娟. 基于组合特征和PSO-BP算法的数字识别[J]. 信息与控制,2011,40(3):375-380.
LUO Yong, HE Xiao-juan. Digital recognition based on combined feature and PSO-BP algorithm[J]. Information and Control, 2011, 32(3): 673-677.
[19] KENNEDY J, EBERHART R. Particle swarm optimization[C] ∥Proceedings of the IEEE International Conference on Neural Networks. Piscatawav: IEEE, 1995: 1942-1948.
[20] MICHAEL W R.Survey of neural network technology for automatic target recognition \[J\]. IEEE Transactions Neural Networks,1990,1(1):28-43.
[21] 刑秀玉,刘鸿宇,黄武. 基于加速度的小波能量特征及样本熵组合的步态分类算法[J]. 传感技术学报,2013,26(4):545-549.
XING Xiu-yu, LIU Hong-yu, HUANG Wu. Gait pattern classification with wavelet energy and sample entropy based on acceleration signals[J]. Chinese Journal of Sensors and Actuators, 2013, 26(4): 545-549.
[22] 苟斌,刘作军,赵丽娜. 基于相关性分析的下肢假肢步行模式预识别方法研究[J]. 东南大学学报:自然科学版,2013,43(S1):192-196.
Gou Bin, Liu Zuo-jun, Zhao Li-na. Walking mode pre-judgment of lower limb prosthesis based on correlation analysis[J]. Journal of Southeast University: Natural Science Edition, 2013, 43(S1):192-196.
[23] YANG Peng, CHEN Ling-ling, GUO Xin, et al. Artificial lower limb with myoelectrical control based on support vector machine[C] ∥ Proceedings of the 6th World Congress on Intelligent Control and Automation. Dalian: [s.n.], 2006: 9486-9489. |