[1] DATTA D, HOWITT J. Conventional versus microchip controlled pneumatic swing phase control for tranfemoral amputees: user’s verdict [J]. Prosthesis and Orthotics International, 1998, 22(2): 129-135.
[2] SUP F, BOHARA A, GOLDFARB M. Design and control of a powered transfemoral prosthesis [J]. International Journal of Robotics Research, 2008, 27(2): 263-273.
[3] HITT J, SUGAR T, HOLGATE M, et al. Robotic transtibial prosthesis with biomechanical energy regeneration [J]. Industrial Robot, 2013,36(5): 441-447.
[4] KAHLE J T, HIGHSMITH M J, HUBBARD S L. Comparison of non-microprocessor knee mechanism versus CLeg on prosthesis evaluation questionnaire, stumbles, falls, walking tests, stair descent, and knee preference [J]. Journal of Rehabilitation Research and Development, 2008, 45(1): 1-14.
[5] DINGWELL J B, DAVIS B L, FRAZIER D M. Use of an instrumented treadmill for real-time gait symmetryevaluation and feedback in normal and transtibial amputee subjects [J]. Prosthetics and Orthotics International, 1996, 20(2): 101-110.
[6] 谭冠政,吴立明.国内外人工腿(假肢)研究的进展及发展趋势 [J].机器人,2001,23(1):91-96.
TAN Guan-zheng, WU Li-ming. Progress and development trend towards study of artificial legs (prostheses) in foreign countries and China [J]. Robot, 2001,23(1): 91-96.
[7] 喻洪流,钱省三,沈凌,等.基于小脑模型神经网络控制的步速跟随智能膝上假肢 [J].中国组织工程研究与临床康复,2007,11(31): 6233-6235.
YU Hong-liu, QIAN Sheng-san, SHEN ling, et al. Intelligent above-knee prosthesis following healthy leg gait with cerebellar model articulation controller [J]. Journal of Clinical Rehabilitative Tissue Engineering Research, 2007, 11(31): 6233-6235.
[8] 王人成.我国假肢技术的研究与进展 [J].中国康复医学杂志,2012, 27(11): 1058-1060.
WANG Ren-cheng. The prosthetic technology research and development of our country [J]. Chinese Journal of Rehabilitation Medicine, 2012, 27(11): 1058-1060.
[9] 高云园,孟明,罗志增,等.利用多源运动信息的下肢假肢多模式多步态识别研究 [J].传感器技术学报.2011, 24(11): 1574-1578.
GAO Yun-yuan, MENG Ming, LUO Zhi-zeng, et al. Multi-Mode and gait phase recognition of lower limb prosthesis based on multi-source motion information [J]. Chinese Journal of Sensors and Actuators, 2011,24(11): 1574-1578.
[10] ZHANG F, LIU M, HUANG H. Effects of locomotion mode recognition errors on volitional control of powered above-knee prostheses [J]. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 2015,23(1): 64-72.
[11] YOUNG A J, SIMON A M, HARGROVE L J. A training method for locomotion mode prediction using powered lower limb prostheses [J]. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 2014, 22(3): 671-677.
[12] 佟丽娜,侯增广,彭亮.基于多路sEMG时序分析的人体运动模式识别方法[J].自动化学报,2014,40(5): 810-820.
TONG Li-na, HOU Zeng-guang, PENG Liang. Multi-channel sEMG time series analysis based human motion recognition method [J]. Acta Automatic Sinica, 2014,40(5): 810-820.
[13] 马玉良,马云鹏,张启忠,等.GABP神经网络在下肢运动步态识别中的应用研究[J].传感技术学报,2013,26(9): 1183-1188.
MA Yu-liang, MA Yun-peng, ZHANG Qi-zhong, et al. Gait phase recognition of lower limb based on GA optimized BP neural network [J]. Chinese Journal of Sensors And Actuators. 2013, 26(9): 1183-1188.
[14] 刘磊,杨鹏,刘作军.基于多源信息和广义回归神经网络的下肢运动模式识别[J].机器人,2015,37(3):310-317.
LIU Lei, YANG Peng, LIU Zuo-jun. Lower limblocomotion modes recognition based on multiple-source information and general regression neural network [J]. Robot. 2015, 37(3): 310-317.
[15] 袁娜,杨鹏,刘作军.利用平均影响值和概率神经网络的步态识别[J].哈尔滨工程大学学报,2015,36(2):1-5.
YUAN Na, YANG Peng, LIU Zuo-jun. Gait recognition based on the mean impact value and probability neural network [J]. Journal of Harbin Engineering University. 2015, 36(2): 1-5.
[16] 齐美彬,王倩,蒋建国.非规范视角步态识别研究[J].仪器仪表学报,2008,29(10):2058-2061.
QI Meibin, WANG Qian, JIANG Jianguo. Research on nonstandard view gait identification [J]. Chinese Journal of Scientific Instrument, 2008, 29 (10):2058-2061.
[17] 佘青山,高云园,孟明.下肢EMG的小波支持向量机多类识别方法[J].华中科技大学学报: 自然科学版,2010,38(10):7579.
SHE Qing-shan, GAO Yun-yuan, MENG Ming. Multiclass recognition of lower limb EMG using wavelet SVM [J]. Journal of Huazhong University of Science and Technology: Natural Science Edition. 2010,38(10):75-79.
[18] TIPPING M E. Sparse bayesian learning and the relevance vector machine [J]. Journal of Machine Learning Research, 2001,1(3): 211-244.
[19] LI W X, LIAN L M. Multiple faces tracking based on relevance vector machine [J]. Journal of Software, 2012, 7(4): 810-813.
[20] 汪洪桥,孙富春,蔡艳宁,等.多核学习方法[J].自动化学报,2010,36(8): 1037-1049.
WANG Hong-qiao, SUN Fu-chun, CAI Yan-ning, et al. On multiple kernel learning methods [J].Acta Automatica Sinica. 2010, 36(8): 1037-1049.
[21] 张凯军,梁循.一种改进的显性多核支持向量机[J].自动化学报,2014,40(10): 2288-2294.
ZHANG Kai-jun, LIANG Dun. An improved domain multiple kernel support vector machine [J]. Acta Automatica Sinica. 2014, 40(10): 2288-2294.
[22] PSORAKIS I, DAMOULAS T, GIROLAMI M A. Multiclass relevance vector machines: sparsity and accuracy [J]. IEEE Transactions on Neural Networks, 2010, 21(10): 1588-1598.
[23] DAMOULAS T, GIROLAMI M A. Combining feature spaces for classification [J]. Pattern Recognition, 2009, 42(11): 2671-2683.
[24] HUANG H, ZHANG F, HARGROVE L J, et al.Continuous locomotionmode identification for prosthetic legs based on neuromuscularmechanical fusion [J]. IEEE Transactions on Biomedical Engineering, 2011, 58(10): 2867-2875.
[25] 陈玲玲.肌电信号的运动模式识别及其在膝上假肢中的应用研究[D].天津:河北工业大学,2010: 56.
CHEN Ling-ling. Motion pattern recogniton based on emg and its application research on ak prosthesis [D]. Tianjin: Hebei University of Technology. 2010: 56.
[26] 丁其川,熊安斌,赵新刚,等.基于表面肌电的运动意图识别方法研究及应用综述[J].自动化学报,2016,42(1): 13-25.
DING Qi-chuan, XIONG An-bin, ZHAO Xin-gang, et al. A review on researches and applications of sEMG-based motion intent recognition methods [J]. Acta Automatica Sinica, 2016, 42(1): 13-25.
[27] 何涛,胡洁,夏鹏,等.基于ReliefF算法与遗传算法的肌电信号特征选择[J].上海交通大学学报, 2016,50(2): 204-208.
HE Tao, ZOU Hu-Jie, XIA Peng, et al. Feature selection of EMG signal based on relieff algorithm andgenetic algorithm [J]. Journal of Shanghai JiaotongUniversity, 2016, 50(2): 204-208.
[28] 龙文,蔡绍洪,焦建军,等.求解约束优化问题的萤火虫算法及其工程应用[J].中南大学学报:自然科学版,2015,46(4): 1260-1267.
LONG Wen, CAI Shao-hong, JIAO Jian-jun, et al. Firefly algorithm for solving constrained optimization problems and engineering applications [J]. Journal of Central South University: Science and Technology, 2015, 46(4): 1260-1267.
[29] 王雪刚,邹早建.基于果蝇优化算法的支持向量机参数优化在船舶操纵预报中的应用[J].上海交通大学学报,2013,47(6): 884-888.
WANG Xue-gang, ZOU Zao-jian. FOA-based SVM parameter optimization and its application in ship manoeuvring prediction [J]. Journal of Shanghai Jiaotong University, 2013, 47(6): 884-888. |