下肢姿态检测及运动状态预测算法研究
|
摘要
智能下肢假肢是近些年来机器人学和生物医学工程领域倍受关注的研究方向,下肢姿态检测及运动状态预测是智能下肢假肢控制的基础。研究智能假肢,为截肢者提供性能优良的假肢,有助于提高截肢者的生活质量,对于构建和谐社会具有重要的意义。目前国外已经出现智能化和仿生度很高的智能下肢假肢,而国内在这一领域的研究还存在较大差距,尚未研制出成熟的智能下肢假肢产品。因此从事智能下肢假肢的探索与研究,对缩短与发达国家的差距,促进我国康复医学工程技术和假肢产业的发展具有重要的意义。
本论文结合“自适应智能膝上假肢关键技术的研究”课题(863项目2008AA04Z212)的部分研究任务展开,针对单侧膝上下肢截肢的残疾人用智能假肢,获取下肢姿态的多源运动信息,采用有效的预测算法进行下肢运动状态的预测,为进一步实现下肢假肢的自适应控制奠定基础。本文具体完成的研究工作有:
(1)针对下肢姿态检测,建立了一个由足底压力、膝关节角度和肌电信号等传感器和测量装置组成的下肢多源运动信息采集系统。具体方法是,运用鞋底嵌入三个PVDF力传感器的方法测取足跟和足趾区域的压力;利用两个ADXL203加速度传感器组合获取膝关节的屈伸角度;运用MyoTrace 400采集仪获取股内侧肌、半腱肌、长收肌、阔筋膜张肌的肌电信号。
(2)根据下肢运动的步态周期规律,给出了一种步态和时相结合的下肢运动状态的划分方法,使之能满足智能下肢假肢运动控制的需要。研究分析足底压力、膝关节角度和肌电三种信号的特点,提出了相应的特征提取方法:对下肢表面肌电信号提出了一种基于相关性分析的特征提取方法;对足底压力信号采用阈值化的判断方法;为了减少膝关节角度信号的处理工作量,以同一步态时相中的膝关节角度均值作为特征。
(3)运用CPN神经网络技术对下肢运动状态进行分类识别。网络输入为多源运动信息中的肌电信号、足底压力和膝关节角度信号等组成的特征向量,网络输出为待识别的运动状态,并将识别结果与课题组成员所提出的方法进行了比较分析。
(4)研究了RBF径向基函数神经网络和ANFIS自适应模糊神经推理系统预测算法,在分析比较两种方法对下肢膝关节角度变化预测的基础上,用ANFIS完成了基于多源运动信息的下肢运动状态预测。实验结果表明,ANFIS能够有效的预测下肢运动状态,预测平均误差能满足下肢运动状态预测精度的需要,为建立下肢运动自适应预测控制系统奠定了基础。
论文取得的主要创新点:
本文建立了基于多源运动信息的下肢运动状态预测系统,提出将肌电信号、足底压力信号和膝关节角度信号的组合特征输入CPN神经网络识别出当前的运动状态,然后将此刻及以前四个时刻的运动状态参数输入ANFIS自适应模糊神经推理系统预测下肢未来的运动状态。
In recent years, intelligent lower limb prosthesis is an attentive research project in the fields of robotics and biomedical engineering, the detection of lower limb’s posture and prediction of motion state is the basis for control of intelligent lower limb prosthesis. To study intelligent prosthesis provides amputees with prosthesis which have good performance to improve their life quality, and has a great significance for constructing harmonious society. Intelligent prosthesis with high capability and bionic degree has been realized overseas, but the domestic research in the field still has a large gap with their study, there is no mature intelligent artificial leg product. The exploration and development of intelligent artificial leg is of great significance to shorten the gap with the developed countries and to promote Rehabilitation Medicine Engineering Technology and prosthesis industry of China.
Combining some research tasks of“the research on the key technology of adaptive intelligent knee prosthesis”(the national 863 Project 2008AA04Z212), this paper aim at the disabilities which have only one knee lower limb amputation to use the intelligent prostheses, obtain multi-source motion information of the lower limb posture, adopt effective prediction algorithms to predict the moving state of lower limb, lay the foundation for the study of Adaptive Motion Control. The major research works are as follows:
(1) For the detection of lower limb posture, a set of system for collection of lower limb’s multi-source motion information was built, which consisted of some sensors and the measurement device of plantar pressure, knee angle and EMG signal. The detailed method was as follows: The shoe attached PVDF force sensor was used to obtain heel and toe regional pressure; two ADXL203 accelerometers was combined to obtain the angle of knee joint; the MyoTrace 400 was used to obtain EMG signals of the Vastus medialis muscle, the semitendinosus muscle, the long adductor muscle and the tensor fascia lata.
(2) According to the law of lower limb movement’s gait cycle, a method combining gait and phase for mode subdivision of lower limb movement was given, which met the need of motion control of the intelligent artificial leg. After researching and analyzing the characteristics of plantar pressure, knee angle and EMG three signals, the corresponding effective feature extraction methods were proposed in this paper: the feature extraction based on correlation analysis was used in EMG; the method of judging based on threshold was used in plantar pressure signal ; in order to reduce the workload of processing the knee angle signal, the mean of the knee angles under the same gait phase was used as the feature of the angle signal of knee joint.
(3) The CPN neural network was used to identify the moving state of lower limb. The input of the network was a concatenation feature vector, consisted of the features of EMG signal, plantar pressure and the knee angle signal. Then the results of identification through the CPN network and the methods given by Task Force members were compared and analyzed.
(4) The prediction algorithms of Radial Basis Function neural network(RBF) and Adaptive Neural Fuzzy Inference System(ANFIS)were studied. On the basis of comparison and analysis of the forecasting results of knee joint angle changes through the two methods, the prediction of the lower limb moving state based on the multi-source motion information was performed by using ANFIS. The experimental results showed that the moving state can be predicted effectively by using ANFIS and the average prediction error met the need of the prediction accuracy for the lower limb moving state, the foundation for the establishment of the lower limb motion’s adaptive prediction control system was laid.
The major innovation is as follow:
The prediction system for the lower limb moving state based on the multi-source motion information was established, the idea was like this: the concatenation feature vector consisted of the features of EMG signal, planter pressure and the knee joint’s angle signal was input to the CPN neural network to identify the current motion state of lower limb, Combined with the first three parameters of motion state to predict future motion state of lower limb by using Adaptive Fuzzy Neural Inference System(ANFIS).
本论文结合“自适应智能膝上假肢关键技术的研究”课题(863项目2008AA04Z212)的部分研究任务展开,针对单侧膝上下肢截肢的残疾人用智能假肢,获取下肢姿态的多源运动信息,采用有效的预测算法进行下肢运动状态的预测,为进一步实现下肢假肢的自适应控制奠定基础。本文具体完成的研究工作有:
(1)针对下肢姿态检测,建立了一个由足底压力、膝关节角度和肌电信号等传感器和测量装置组成的下肢多源运动信息采集系统。具体方法是,运用鞋底嵌入三个PVDF力传感器的方法测取足跟和足趾区域的压力;利用两个ADXL203加速度传感器组合获取膝关节的屈伸角度;运用MyoTrace 400采集仪获取股内侧肌、半腱肌、长收肌、阔筋膜张肌的肌电信号。
(2)根据下肢运动的步态周期规律,给出了一种步态和时相结合的下肢运动状态的划分方法,使之能满足智能下肢假肢运动控制的需要。研究分析足底压力、膝关节角度和肌电三种信号的特点,提出了相应的特征提取方法:对下肢表面肌电信号提出了一种基于相关性分析的特征提取方法;对足底压力信号采用阈值化的判断方法;为了减少膝关节角度信号的处理工作量,以同一步态时相中的膝关节角度均值作为特征。
(3)运用CPN神经网络技术对下肢运动状态进行分类识别。网络输入为多源运动信息中的肌电信号、足底压力和膝关节角度信号等组成的特征向量,网络输出为待识别的运动状态,并将识别结果与课题组成员所提出的方法进行了比较分析。
(4)研究了RBF径向基函数神经网络和ANFIS自适应模糊神经推理系统预测算法,在分析比较两种方法对下肢膝关节角度变化预测的基础上,用ANFIS完成了基于多源运动信息的下肢运动状态预测。实验结果表明,ANFIS能够有效的预测下肢运动状态,预测平均误差能满足下肢运动状态预测精度的需要,为建立下肢运动自适应预测控制系统奠定了基础。
论文取得的主要创新点:
本文建立了基于多源运动信息的下肢运动状态预测系统,提出将肌电信号、足底压力信号和膝关节角度信号的组合特征输入CPN神经网络识别出当前的运动状态,然后将此刻及以前四个时刻的运动状态参数输入ANFIS自适应模糊神经推理系统预测下肢未来的运动状态。
In recent years, intelligent lower limb prosthesis is an attentive research project in the fields of robotics and biomedical engineering, the detection of lower limb’s posture and prediction of motion state is the basis for control of intelligent lower limb prosthesis. To study intelligent prosthesis provides amputees with prosthesis which have good performance to improve their life quality, and has a great significance for constructing harmonious society. Intelligent prosthesis with high capability and bionic degree has been realized overseas, but the domestic research in the field still has a large gap with their study, there is no mature intelligent artificial leg product. The exploration and development of intelligent artificial leg is of great significance to shorten the gap with the developed countries and to promote Rehabilitation Medicine Engineering Technology and prosthesis industry of China.
Combining some research tasks of“the research on the key technology of adaptive intelligent knee prosthesis”(the national 863 Project 2008AA04Z212), this paper aim at the disabilities which have only one knee lower limb amputation to use the intelligent prostheses, obtain multi-source motion information of the lower limb posture, adopt effective prediction algorithms to predict the moving state of lower limb, lay the foundation for the study of Adaptive Motion Control. The major research works are as follows:
(1) For the detection of lower limb posture, a set of system for collection of lower limb’s multi-source motion information was built, which consisted of some sensors and the measurement device of plantar pressure, knee angle and EMG signal. The detailed method was as follows: The shoe attached PVDF force sensor was used to obtain heel and toe regional pressure; two ADXL203 accelerometers was combined to obtain the angle of knee joint; the MyoTrace 400 was used to obtain EMG signals of the Vastus medialis muscle, the semitendinosus muscle, the long adductor muscle and the tensor fascia lata.
(2) According to the law of lower limb movement’s gait cycle, a method combining gait and phase for mode subdivision of lower limb movement was given, which met the need of motion control of the intelligent artificial leg. After researching and analyzing the characteristics of plantar pressure, knee angle and EMG three signals, the corresponding effective feature extraction methods were proposed in this paper: the feature extraction based on correlation analysis was used in EMG; the method of judging based on threshold was used in plantar pressure signal ; in order to reduce the workload of processing the knee angle signal, the mean of the knee angles under the same gait phase was used as the feature of the angle signal of knee joint.
(3) The CPN neural network was used to identify the moving state of lower limb. The input of the network was a concatenation feature vector, consisted of the features of EMG signal, plantar pressure and the knee angle signal. Then the results of identification through the CPN network and the methods given by Task Force members were compared and analyzed.
(4) The prediction algorithms of Radial Basis Function neural network(RBF) and Adaptive Neural Fuzzy Inference System(ANFIS)were studied. On the basis of comparison and analysis of the forecasting results of knee joint angle changes through the two methods, the prediction of the lower limb moving state based on the multi-source motion information was performed by using ANFIS. The experimental results showed that the moving state can be predicted effectively by using ANFIS and the average prediction error met the need of the prediction accuracy for the lower limb moving state, the foundation for the establishment of the lower limb motion’s adaptive prediction control system was laid.
The major innovation is as follow:
The prediction system for the lower limb moving state based on the multi-source motion information was established, the idea was like this: the concatenation feature vector consisted of the features of EMG signal, planter pressure and the knee joint’s angle signal was input to the CPN neural network to identify the current motion state of lower limb, Combined with the first three parameters of motion state to predict future motion state of lower limb by using Adaptive Fuzzy Neural Inference System(ANFIS).
引文
[1]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.
[2] D. Datta, B. Heller, J. Howitt. A comparative evaluation of oxygen consumption and gait pattern in amputees using Intelligent Prostheses and conventionally damped knee swing-phase control [J]. Clinical Rehabilitation, 2005, 19(4):398-403.
[3] S. Zahedi, A. Sykes, S. Lang, I. Cullington. Adaptive prosthesis–a new concept in prosthetic knee control [J]. Robotic, 2005, 23(3):337-344.
[4] M. S. Orendurff, A. D. Segal, G. K. Klute, M. L. McDowell, J. A. Pecoraro, J. M. Czerniecki. Gait efficiency using the C-Leg [J]. Journal of Rehabilitation Research and Development, 2006, 43(2):239-246.
[5]杨建坤,季林红,王人成,金德闻,张济川.四杆机构膝关节控制力矩分析[J].中国康复理论与实践,2004,10(5):264-265.
[6] D. Jin, J. Yang, R. Zhang, R. Wang, J. Zhang. Terrain identification for prosthetic knees based on electromyography signal features [J]. Tsinghua Science and Technology, 2006, 11(1):74-79.
[7]杨义勇,王人成,郝智秀,金德闻,张涵.自然步态摆动期动力学协调模式的研究[J].生物医学工程杂志,2006,23(1):69-73.
[8]谭冠政,何胜军,曾庆冬,闫炳雷,蔡光超.CIP-I智能仿生人工腿步速测量系统研究与设计[J].计算机测量与控制,2005,13(11):1164-1166.
[9]闫炳雷,谭冠军,曾庆冬,谭冠政.智能仿生人工腿CIP-I Leg性能仿真评估研究[J].计算机仿真,2006,23(9).231-235
[10]郭欣,杨鹏,王志宇.肌电信号控制下肢假肢的机理[J].中南工业大学学报,2003,34(2):31-33.
[11] Xin Guo, Peng Yang, Ying Li, Wei-Li Yan. The SEMG Analysis For the Lower Limb Prosthesis Using Wavelet Transformation[C]. Proceedings of the 26th Annual International Conference of the IEEE EMBS. San Francisco, CA, USA* September 1-5, 2004 341-344
[12] Peng Yang, Lingling Chen, Xin Guo, Xitai Wang, Lifeng Li. Artificial Lower Limb with Myoelectrical Control Based on Support Vector Machine [J]. Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21 - 23, Dalian, China, 2006: 9486-9489.
[13]王新宪.中国残疾人康复事业的现状与展望. 2001年中国残疾人康复论坛专家论文集. 2001,(7):1-6.
[14]陈光.我国残疾人用品生产和供应概况及发展趋势. 2001年中国残疾人康复论坛专家论文集. 2001, (7):12-17.
[15] Vapnik, V. N. The Nature of Statistical Learning Theory[M].John Viley & Sons. Inc.N.Y,1998.
[16]张学工.统计学习理论和支持向量机[J].自动化学报, 2000,26(1):32-42.
[17]王定成.支持向量机回归理论与控制的综述[J].模式识别与人工智能,2003,16(2):192-197.
[18]高剑.基于小波系数矩阵奇异值分解和SVM的表面肌电多运动模式识别研究[D].杭州:杭州电子科技大学硕士学位论文,2005.
[19]杨广映.带触觉仿生假手研究[D].杭州:杭州电子科技大学硕士学位论文,2005.
[20] Wang W. The Theory and Application of Generalized Predictive Control [M].Beijing: Beijing Science Press, 1998:121.
[21] Zhou X D, Du C B. Improved generalized predictive control method [J]. Southeast Univ Nat Sci Ed, 2006, 36 (2): 316.
[22] Tsujil T. Pattern classification of time-series EMG signals using neural networks [J].International Journal of Adaptive Control and Signal Processing, 2000, 14: 829 -848.
[23] CHEN Lei, ZHANG Qiaoling, Q IWeigui, etal. Heatload prediction for heat supply system based on RBF neural network and time series crossover [C] International Conference on Machine Learning and Cybernetics, Kunming: Yunan University, 2008: 784 - 788.
[24] ZHANG Yun, ZHOU Quan, SUN Caixin, et a l. RBFneural network and ANF IS2based short2term load fore2casting app roach in real2time p rice environment [ J ].IEEE Transactions on Power Systems. 2008, 23 ( 3 ) :853 - 858.
[25]刘志杰,季令,叶玉玲等.基于径向基神经网络的铁路货运量预测[J].铁道学报,2006, 28 (5): 1 - 5.
[26] HaganM T, Behr SM. The time series approach to short term load forecasting [J]. IEEE Transaction on Power Systems.1987, 2 (3): 785- 791.
[27] Papalexopoulos A D, Hester berg T C. A regression2 based app roach to short-term system load forecasting [J]. IEEE Transaction on Power Systems, 1990, 5(4): 1535-1544.
[28] Lee KY. Short-term load forecasting using an artificial neural network [J]. IEEE Transaction on PowerSystems.1991, 7(1):124- 132.
[29] HAYKIN S.神经网络原理[M ].北京:机械工业出版社, 2004:20-21.
[30]乔俊飞,孙雅明.基于模糊预测的自适应控制方法及应用[J].电工技术学报,2000,15(4): 67-70.
[31] Jang J S.ANFIS: Adaptive network-based fuzzy inference systems. IEEE Transaction on System, Man and cybernetics, 1993, 23(3):665–685.
[32] Bates J M, Granger C W J. Combination of Forecasts. Operations Research Quarterly, 1969, 20(4):114-118.
[33]郑秀缓,贾书惠,高云峰等.现代运动生物力学[M].北京:国防工业出版社,2002.10.
[34]洪炳熔,贺怀清.虚拟人的步行和跑步运动控制方法的研究[J].高技术通讯,2001(9): 91-95.
[35]张瑞红,金德闻,张济川,王人成,马琳.不同路况下正常步态特征研究[J].清华大学学报(自然科学版),2000,40(8):77-80.
[36]罗志增,任晓亮.肌电信号的拾取和预处理[J].传感技术学报,2004,6(2):220-223.
[37]罗志增,王人成.具有触觉和肌电控制功能的仿生假手研究[J].传感技术学报,2005,18(1):23-27.
[38] Muzumdar, Ashok. Powered Upper Limb Prostheses-Control [J], Implementation and Clinical Application. Springer-Verlag Berlin and Heidelberg GmbH & Co. K. 2004.
[39]卢祖能,曾庆杏,李承晏,余绍祖.肌电图学[M].北京:人民卫生出版社,2000: 169-172.
[40]周洋,万建国,淘宝祺. PVDF压电薄膜的结构机理与应用[J].材料导报,1996,5: 43-47.
[41]刘刚,王立香,张连俊. Labview 8.20中文版编程及应用[M].北京:电子工业出版社,2008: 315-318.
[42] J. M. Jasiewicz, J. H. J. Allum, J. W. Middleton, A. Barriskill, P. Condie, B. Purcell, R. C. T. Li. Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals[J].Gait & Posture, 2006, 24(4): 502-509.
[43] Simona Iftime Nielsen, Mirjana B. Popovic, Dejan B. Popovi. Estimation of joint angles with capacitive accelerometers[C]. Proceedings of 2008 Annual IEEE Conference, Aalborg, 2008: 1-4.
[44] Hooman Dejnabadi, Brigitte M. Jolles, and Kamiar Aminian. A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes [J]. IEEE transactions on biomedical engineering, 2005, 52(5): 1478-1484.
[45] I. P. I. Pappas, M. R. Popovic, T. Keller, V. Dietz, M. Morari. (2001) A reliable gait phase detection system [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2001, 9(2): 113-125.
[46] Sergio Scapellato, Filippo Cavallo, Chiara Martelloni, Angelo M. Sabatini. In-use calibration of body-mounted gyroscopes for applications in gait analysis [J]. Sensors and Actuators, 2005, A123-124.418–422.
[47]王岚,张今瑜,王劲松.人体步态相位检测实验研究[J].传感器与微系统, 2006, 25(5): 42-44.
[48] Madhavi M. Patil, Otto Prohaska. Fiber optic sensor for joint angle measurement[C]. IEEE engineering in medicine & biology 10th international conference, 1988: 803-804.
[49]李爽,罗志增,孟明.基于加速度传感器的下肢运动信息获取方法[A].机电工程, 2009,26(1):5-7.
[50]鹿麟,林凌,李刚ADXL203型双轴加速计在倾斜度测量中的应用[B].国外电子元器件,2007(7): 61-64.
[51]洪炳熔,贺怀清.虚拟人的步行和跑步运动控制方法的研究[J].高技术通讯, 2001,3: 91-95.
[52]杨福生.小波变换的工程分析与应用[M].北京:科学出版社,2000: 23-29.
[53]孙延奎.小波分析及其应用[M].北京:机械工业出版社,2005: 34-36.
[54]陈晓楠,索继东.空域相关滤波法在突变检测中的应用[J].大连海事大学学报,2007,33(12):139-141.
[55]闻新等.MATLAB神经网络仿真与应用[M].北京:科学出版社,2003.
[56] Xu yansun, et al. Wavelet transform domain filters: a spatially selective noise filtration technique[J].IEEE Trans. Image Processing, 1994, 3(6):747-758.
[57]贾雪琴,王旭等.基于小波变换和K-L展开的单通道表面肌电信号识别[J].东北大学学报(自然科学版),2006,27(8): 859-862.
[58]高大启.有教师的线性基本函数前向三层神经网络结构研究[J].计算机学报,1998,21(1): 80-85.
[59] Piggio T, Girosi F. A theory of network s for approximation and learning. AIM emo No 1140, Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Cambridge Mass. 1989.
[60] Monica Bianchini, etal. Learning without local minima in radial basis function network s[J].IEEE Trans on Neural Network.1995. 6930: 749～755.
[61] Moody J, Darken C. Fast learning in networks of locally-turned processing units[J]. Neural Computation, 1989, (1): 281～294.
[62] Bruce A, etc. Cooperative-competitive genetic evolution of radial basis function centers and width s for time series prediction [J].IEEE Transactions on Networks, 1996, 7(4): 868～880.
[63]曹先彬,高隽,王熙法.基于生态竞争模型的遗传强化学习[J].软件学报,1999, 10(6): 658～661.
[64] Jang J S.ANFIS: Adaptive network-based fuzzy inference systems. IEEE Transaction on System, Man and cybernetics,1993, 23(3):665–685.
[65] Lotfi A Z, Berkeley C.Fuzzy Logic Toolbox for Use with MATLAB User’s Guide. MA: The Math Works Inc, 2005: 4-126.
[66] Hagan, M. T, H. B. Demuth, and M. H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996, 12-1–12-30.
[67] Demuth, H, Beale, M, Hagan, M. Neural Network Toolbox for use with MATLAB User's Guide.4th edn. The Math Works Inc, MA, 2005, 5-14 and 5-74.
[68] Moller M.F.A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 1993, 6(4):525–533.
[69] Falas T, Stafylopatis A. Implementing temporal-difference learning with the scaled conjugate gradient algorithm. Neural Processing Letters, 2005, 22(3):361–375.
[2] D. Datta, B. Heller, J. Howitt. A comparative evaluation of oxygen consumption and gait pattern in amputees using Intelligent Prostheses and conventionally damped knee swing-phase control [J]. Clinical Rehabilitation, 2005, 19(4):398-403.
[3] S. Zahedi, A. Sykes, S. Lang, I. Cullington. Adaptive prosthesis–a new concept in prosthetic knee control [J]. Robotic, 2005, 23(3):337-344.
[4] M. S. Orendurff, A. D. Segal, G. K. Klute, M. L. McDowell, J. A. Pecoraro, J. M. Czerniecki. Gait efficiency using the C-Leg [J]. Journal of Rehabilitation Research and Development, 2006, 43(2):239-246.
[5]杨建坤,季林红,王人成,金德闻,张济川.四杆机构膝关节控制力矩分析[J].中国康复理论与实践,2004,10(5):264-265.
[6] D. Jin, J. Yang, R. Zhang, R. Wang, J. Zhang. Terrain identification for prosthetic knees based on electromyography signal features [J]. Tsinghua Science and Technology, 2006, 11(1):74-79.
[7]杨义勇,王人成,郝智秀,金德闻,张涵.自然步态摆动期动力学协调模式的研究[J].生物医学工程杂志,2006,23(1):69-73.
[8]谭冠政,何胜军,曾庆冬,闫炳雷,蔡光超.CIP-I智能仿生人工腿步速测量系统研究与设计[J].计算机测量与控制,2005,13(11):1164-1166.
[9]闫炳雷,谭冠军,曾庆冬,谭冠政.智能仿生人工腿CIP-I Leg性能仿真评估研究[J].计算机仿真,2006,23(9).231-235
[10]郭欣,杨鹏,王志宇.肌电信号控制下肢假肢的机理[J].中南工业大学学报,2003,34(2):31-33.
[11] Xin Guo, Peng Yang, Ying Li, Wei-Li Yan. The SEMG Analysis For the Lower Limb Prosthesis Using Wavelet Transformation[C]. Proceedings of the 26th Annual International Conference of the IEEE EMBS. San Francisco, CA, USA* September 1-5, 2004 341-344
[12] Peng Yang, Lingling Chen, Xin Guo, Xitai Wang, Lifeng Li. Artificial Lower Limb with Myoelectrical Control Based on Support Vector Machine [J]. Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21 - 23, Dalian, China, 2006: 9486-9489.
[13]王新宪.中国残疾人康复事业的现状与展望. 2001年中国残疾人康复论坛专家论文集. 2001,(7):1-6.
[14]陈光.我国残疾人用品生产和供应概况及发展趋势. 2001年中国残疾人康复论坛专家论文集. 2001, (7):12-17.
[15] Vapnik, V. N. The Nature of Statistical Learning Theory[M].John Viley & Sons. Inc.N.Y,1998.
[16]张学工.统计学习理论和支持向量机[J].自动化学报, 2000,26(1):32-42.
[17]王定成.支持向量机回归理论与控制的综述[J].模式识别与人工智能,2003,16(2):192-197.
[18]高剑.基于小波系数矩阵奇异值分解和SVM的表面肌电多运动模式识别研究[D].杭州:杭州电子科技大学硕士学位论文,2005.
[19]杨广映.带触觉仿生假手研究[D].杭州:杭州电子科技大学硕士学位论文,2005.
[20] Wang W. The Theory and Application of Generalized Predictive Control [M].Beijing: Beijing Science Press, 1998:121.
[21] Zhou X D, Du C B. Improved generalized predictive control method [J]. Southeast Univ Nat Sci Ed, 2006, 36 (2): 316.
[22] Tsujil T. Pattern classification of time-series EMG signals using neural networks [J].International Journal of Adaptive Control and Signal Processing, 2000, 14: 829 -848.
[23] CHEN Lei, ZHANG Qiaoling, Q IWeigui, etal. Heatload prediction for heat supply system based on RBF neural network and time series crossover [C] International Conference on Machine Learning and Cybernetics, Kunming: Yunan University, 2008: 784 - 788.
[24] ZHANG Yun, ZHOU Quan, SUN Caixin, et a l. RBFneural network and ANF IS2based short2term load fore2casting app roach in real2time p rice environment [ J ].IEEE Transactions on Power Systems. 2008, 23 ( 3 ) :853 - 858.
[25]刘志杰,季令,叶玉玲等.基于径向基神经网络的铁路货运量预测[J].铁道学报,2006, 28 (5): 1 - 5.
[26] HaganM T, Behr SM. The time series approach to short term load forecasting [J]. IEEE Transaction on Power Systems.1987, 2 (3): 785- 791.
[27] Papalexopoulos A D, Hester berg T C. A regression2 based app roach to short-term system load forecasting [J]. IEEE Transaction on Power Systems, 1990, 5(4): 1535-1544.
[28] Lee KY. Short-term load forecasting using an artificial neural network [J]. IEEE Transaction on PowerSystems.1991, 7(1):124- 132.
[29] HAYKIN S.神经网络原理[M ].北京:机械工业出版社, 2004:20-21.
[30]乔俊飞,孙雅明.基于模糊预测的自适应控制方法及应用[J].电工技术学报,2000,15(4): 67-70.
[31] Jang J S.ANFIS: Adaptive network-based fuzzy inference systems. IEEE Transaction on System, Man and cybernetics, 1993, 23(3):665–685.
[32] Bates J M, Granger C W J. Combination of Forecasts. Operations Research Quarterly, 1969, 20(4):114-118.
[33]郑秀缓,贾书惠,高云峰等.现代运动生物力学[M].北京:国防工业出版社,2002.10.
[34]洪炳熔,贺怀清.虚拟人的步行和跑步运动控制方法的研究[J].高技术通讯,2001(9): 91-95.
[35]张瑞红,金德闻,张济川,王人成,马琳.不同路况下正常步态特征研究[J].清华大学学报(自然科学版),2000,40(8):77-80.
[36]罗志增,任晓亮.肌电信号的拾取和预处理[J].传感技术学报,2004,6(2):220-223.
[37]罗志增,王人成.具有触觉和肌电控制功能的仿生假手研究[J].传感技术学报,2005,18(1):23-27.
[38] Muzumdar, Ashok. Powered Upper Limb Prostheses-Control [J], Implementation and Clinical Application. Springer-Verlag Berlin and Heidelberg GmbH & Co. K. 2004.
[39]卢祖能,曾庆杏,李承晏,余绍祖.肌电图学[M].北京:人民卫生出版社,2000: 169-172.
[40]周洋,万建国,淘宝祺. PVDF压电薄膜的结构机理与应用[J].材料导报,1996,5: 43-47.
[41]刘刚,王立香,张连俊. Labview 8.20中文版编程及应用[M].北京:电子工业出版社,2008: 315-318.
[42] J. M. Jasiewicz, J. H. J. Allum, J. W. Middleton, A. Barriskill, P. Condie, B. Purcell, R. C. T. Li. Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals[J].Gait & Posture, 2006, 24(4): 502-509.
[43] Simona Iftime Nielsen, Mirjana B. Popovic, Dejan B. Popovi. Estimation of joint angles with capacitive accelerometers[C]. Proceedings of 2008 Annual IEEE Conference, Aalborg, 2008: 1-4.
[44] Hooman Dejnabadi, Brigitte M. Jolles, and Kamiar Aminian. A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes [J]. IEEE transactions on biomedical engineering, 2005, 52(5): 1478-1484.
[45] I. P. I. Pappas, M. R. Popovic, T. Keller, V. Dietz, M. Morari. (2001) A reliable gait phase detection system [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2001, 9(2): 113-125.
[46] Sergio Scapellato, Filippo Cavallo, Chiara Martelloni, Angelo M. Sabatini. In-use calibration of body-mounted gyroscopes for applications in gait analysis [J]. Sensors and Actuators, 2005, A123-124.418–422.
[47]王岚,张今瑜,王劲松.人体步态相位检测实验研究[J].传感器与微系统, 2006, 25(5): 42-44.
[48] Madhavi M. Patil, Otto Prohaska. Fiber optic sensor for joint angle measurement[C]. IEEE engineering in medicine & biology 10th international conference, 1988: 803-804.
[49]李爽,罗志增,孟明.基于加速度传感器的下肢运动信息获取方法[A].机电工程, 2009,26(1):5-7.
[50]鹿麟,林凌,李刚ADXL203型双轴加速计在倾斜度测量中的应用[B].国外电子元器件,2007(7): 61-64.
[51]洪炳熔,贺怀清.虚拟人的步行和跑步运动控制方法的研究[J].高技术通讯, 2001,3: 91-95.
[52]杨福生.小波变换的工程分析与应用[M].北京:科学出版社,2000: 23-29.
[53]孙延奎.小波分析及其应用[M].北京:机械工业出版社,2005: 34-36.
[54]陈晓楠,索继东.空域相关滤波法在突变检测中的应用[J].大连海事大学学报,2007,33(12):139-141.
[55]闻新等.MATLAB神经网络仿真与应用[M].北京:科学出版社,2003.
[56] Xu yansun, et al. Wavelet transform domain filters: a spatially selective noise filtration technique[J].IEEE Trans. Image Processing, 1994, 3(6):747-758.
[57]贾雪琴,王旭等.基于小波变换和K-L展开的单通道表面肌电信号识别[J].东北大学学报(自然科学版),2006,27(8): 859-862.
[58]高大启.有教师的线性基本函数前向三层神经网络结构研究[J].计算机学报,1998,21(1): 80-85.
[59] Piggio T, Girosi F. A theory of network s for approximation and learning. AIM emo No 1140, Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Cambridge Mass. 1989.
[60] Monica Bianchini, etal. Learning without local minima in radial basis function network s[J].IEEE Trans on Neural Network.1995. 6930: 749～755.
[61] Moody J, Darken C. Fast learning in networks of locally-turned processing units[J]. Neural Computation, 1989, (1): 281～294.
[62] Bruce A, etc. Cooperative-competitive genetic evolution of radial basis function centers and width s for time series prediction [J].IEEE Transactions on Networks, 1996, 7(4): 868～880.
[63]曹先彬,高隽,王熙法.基于生态竞争模型的遗传强化学习[J].软件学报,1999, 10(6): 658～661.
[64] Jang J S.ANFIS: Adaptive network-based fuzzy inference systems. IEEE Transaction on System, Man and cybernetics,1993, 23(3):665–685.
[65] Lotfi A Z, Berkeley C.Fuzzy Logic Toolbox for Use with MATLAB User’s Guide. MA: The Math Works Inc, 2005: 4-126.
[66] Hagan, M. T, H. B. Demuth, and M. H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996, 12-1–12-30.
[67] Demuth, H, Beale, M, Hagan, M. Neural Network Toolbox for use with MATLAB User's Guide.4th edn. The Math Works Inc, MA, 2005, 5-14 and 5-74.
[68] Moller M.F.A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 1993, 6(4):525–533.
[69] Falas T, Stafylopatis A. Implementing temporal-difference learning with the scaled conjugate gradient algorithm. Neural Processing Letters, 2005, 22(3):361–375.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |