基于电机辅助功能性电刺激脚踏车康复训练系统的研究
|
摘要
功能性电刺激(Functional Electrical Stimulation, FES)是一种利用电极刺激失去神经控制的肌肉,使肌肉产生收缩,从而恢复人体特定部位功能的交叉前沿康复技术。FES技术在医疗及康复工程等领域有多种应用形式,FES脚踏车(FES Cycling)系统则是基于FES技术的、集成了机械、控制和康复工程的一门前沿课题,其主旨在于使患者能够主动训练,而且该系统可实现代步功能。这将有利于提高患者的心肺功能,增加肌肉强度和厚度,使患者部分运动功能得到重建;同时这还将有助于减轻患者家庭负担,提高患者治愈病患的信心,其康复效果已被大量的实验证明。本研究工作以FES脚踏车技术的实现出发,从FES脚踏车的构建、模型及控制等角度进行了研究。仿真和试验结果表明,该系统能够很好地实现对患者的康复训练,达到了预期的设计目标。
本文详细综述了FES脚踏车康复训练系统的各个技术点的国内外研究现状,在参考已有技术特点的基础上,开展了以构建FES脚踏车康复训练系统为目的的研究。首先概述了FES脚踏车康复训练系统的性能特点,该系统的控制由两个闭环控制来实现,在结构上分为五大部分;从系统的硬件和软件构成等角度详细论述了各个部分工作原理及结构。为了深入研究患者在蹬踏训练过程中人体与系统相互作用的原理,建立了系统的动力学模型。该模型由运动学模型、电刺激—肌肉力模型及动力学模型组成;通过建立仿真模型得到了在系统克服腿部自重情况下的仿真结果,并将仿真结果与试验结果进行了对比,验证了该模型的准确性。在此基础上,建立了以速度和脉宽为输入数据的动力学数学和仿真模型,获得了在电机辅助给定速度和脉宽情况下的肌肉输出力矩;以辨识试验和阶跃响应试验中的速度和脉宽信号为输入数据进行了仿真,仿真结果与试验结果的对比分析表明,该模型具有较好的正确性。
刺激模式的好坏直接决定了训练效果,本文首先建立了FES脚踏车康复训练系统的蹬踏运动学模型,获得了各个关节以曲柄角度的表示的极限值,通过分析在各个极限角度范围内的各个关节的状态,以及综合各个肌肉群收缩对关节状态的作用,获得了零极限刺激模式,并以此为基础建立了以肌肉蹬踏力矩最大和尽量避免肌肉群共同作用造成关节相矛盾的运动为优化目的的试验优化方法,并获得了优化结果。为了对试验优化方法进行检验,本文还建立了刺激模式的理论优化方法。以最大输出功率和最小肌肉力为目标,建立了优化目标函数,获得了在各个曲柄速度下三组肌肉群的起始角度和终止角度的优化结果。由于肌肉对电刺激反应存在延迟,通过分析肌肉电刺激延迟反应,引入了延迟常数对刺激模式进行修正。并对蹬踏运动进行了分析,确定了影响蹬踏运动波动的因素。
为了实现FES脚踏车康复训练系统的恒功率输出,从速度和功率两个闭环入手,建立了以系统辨识为基础的系统模型,并进行了占空比—速度模型和脉宽—功率模型的辨识试验,获取了辨识的输入输出数据,按照选取的模型类及规则确定了模型结构和参数;通过模型输出与实际试验输出数据对比,验证了辨识模型的正确性。通过分析占空比—速度模型和脉宽—功率模型的开环性能,确定了使用RST的控制策略的闭环控制。在确定闭环控制器的性能指标后,利用RST算法设计出控制器,并对控制器的性能进行了仿真和试验研究,结果表明控制器能够达到设计指标,且其稳定性、抗干扰性及跟踪性能均达到要求。考虑到功率闭环中人体疲劳等因素对脉宽功率模型的影响,设计了具有模糊和PID共有特点的模糊PID自校正控制器,仿真和试验结果表明,系统的跟踪性能和抗干扰性能均符合要求。此外,使用模糊T-S控制策略,通过辨识人体不疲劳和疲劳两种状态下的模型,分别设计了两种状态的线性二次型最优控制器和改进RST控制器,将系统两种状态的控制器通过模糊T-S进行融合,使系统能够在不疲劳、疲劳以及中间状态能够自动按照比例输出控制量,仿真结果表明达到设计预期目标。
Functional Electrical Stimulation (FES) is a cross-frontier rehabilitation technology that using electrical impulse via electrodes to stimulate the muscles that are loss of nerve control to contract, thus rebuild their function of related limbs and organs of the body. FES technology has been applied in a variety of areas with various forms. FES cycling system is based on FES technology and integrated with machinery, control and rehabilitation techniques, which can make patients realize the voluntary exercise and assistant walk. Patients may benefit from the exercise for the improvement of cardiorespiratory function, the increase of muscular strength and thickness, and the rebuilding of partial function. Furthermore, it may relieve patients from the family burden and strengthen their confidence to overcome disease. The advantages have been proved by numerous experiments. In the present study, bases on the reality of FES cycling technology, the FES cycling system was constructed and modeled, and the related control strategy was studied. The simulated and experimental results indicate that the system functioned well in the rehabilitation training of patients and achieved the proposed goal.
Firstly, this paper reviews the various techniques of FES cycling system. Based on the present advantages of the cycling system, a FES cycling system based on motor assistance was studied. The characteristics of the system were summarized: it consists of two close loops for control and five structural parts. Constitutions and working principles of every part were introduced in detail with the viewpoint of hardware and software. A dynamic model was built to describe the interaction between the object and the system during the training process. In the model, the kinematical model, electrical stimulation - muscle force model and dynamics model were integrated. Then simulation was conducted under the circumstance of overcoming leg weight. The comparison between the simulated and experimental results verified the accuracy of the model. Based on this analysis, a dynamic model and a simulation model with velocity and pulse width of assistant motor as input data were established to get the muscles output torque. The velocity and pulse width in the identification test and step response test were used in the simulation. The simulation results are in accordance with experimental ones which indicate that the model is with content validity.
Secondly, the system cycling kinematics model was established in this study to get the limit of the angle of related joints in terms of crank angles, which are essential to determine the joint states. Synthesized the effect of muscles contraction to joint state, a zero limit stimulation pattern was obtained to optimize the maximum muscle cadence torque and avoid joints contradictory movement caused by muscle. To testify the experimental optimization method with the aim to obtain the maximum output torque and the minimum muscle force, the optimal objective function was constructed to get the optimization results of initial and terminated angles for three groups of muscles under various crank velocities. With consideration of the delay of the response of simulated muscles, a delay factor was introduced in the model to modify the stimulating patterns. Moreover, the hacking movement was analyzed and its influence factors were determined.
Finally, in order to realize the constant power output of FES cycling system, a systematic identification-based model was developed in the respect of two closed loops, velocity and power. The identifications related to PWM - speed model and the pulse width-power model were tested and the model parameters and the structure in accordance with the selected modal patterns and rules were obtained. The comparison of model output and the actual output data testifies the accuracy of identification model. The RST control strategy of the closed-loop control was used in the study based on the analysis of the open-loop performance of two models. Then, a controller was designed based on the RST algorithm after the determination of the performance specification of the closed loop controller. The simulation and experiments results show that the parameters of controller can satisfy the design requirements, and its stability, anti-disturbance and tracking performance are good. With consideration of the effect of the muscles fatigue on the pulse width - power model, a fuzzy PID auto-adjusted controller was designed with integration of advantages of fussy system and PID. The tracking and anti-disturbance performance of this controller was testified by experimental and simulated results. Besides, a LQR controller and a modified RST controller were designed using fuzzy T-S controlling strategy, based on the identification of the models for the fatigue and non-fatigue states of patients respectively. Whereat, these controllers for different state were integrated through T-S techniques to satisfy the requirements of system so that it can generate output automatically and proportionally when it works at non-fatigue, fatigue states and in-between. The simulated results indicate that the design of the controller is in good use.
本文详细综述了FES脚踏车康复训练系统的各个技术点的国内外研究现状,在参考已有技术特点的基础上,开展了以构建FES脚踏车康复训练系统为目的的研究。首先概述了FES脚踏车康复训练系统的性能特点,该系统的控制由两个闭环控制来实现,在结构上分为五大部分;从系统的硬件和软件构成等角度详细论述了各个部分工作原理及结构。为了深入研究患者在蹬踏训练过程中人体与系统相互作用的原理,建立了系统的动力学模型。该模型由运动学模型、电刺激—肌肉力模型及动力学模型组成;通过建立仿真模型得到了在系统克服腿部自重情况下的仿真结果,并将仿真结果与试验结果进行了对比,验证了该模型的准确性。在此基础上,建立了以速度和脉宽为输入数据的动力学数学和仿真模型,获得了在电机辅助给定速度和脉宽情况下的肌肉输出力矩;以辨识试验和阶跃响应试验中的速度和脉宽信号为输入数据进行了仿真,仿真结果与试验结果的对比分析表明,该模型具有较好的正确性。
刺激模式的好坏直接决定了训练效果,本文首先建立了FES脚踏车康复训练系统的蹬踏运动学模型,获得了各个关节以曲柄角度的表示的极限值,通过分析在各个极限角度范围内的各个关节的状态,以及综合各个肌肉群收缩对关节状态的作用,获得了零极限刺激模式,并以此为基础建立了以肌肉蹬踏力矩最大和尽量避免肌肉群共同作用造成关节相矛盾的运动为优化目的的试验优化方法,并获得了优化结果。为了对试验优化方法进行检验,本文还建立了刺激模式的理论优化方法。以最大输出功率和最小肌肉力为目标,建立了优化目标函数,获得了在各个曲柄速度下三组肌肉群的起始角度和终止角度的优化结果。由于肌肉对电刺激反应存在延迟,通过分析肌肉电刺激延迟反应,引入了延迟常数对刺激模式进行修正。并对蹬踏运动进行了分析,确定了影响蹬踏运动波动的因素。
为了实现FES脚踏车康复训练系统的恒功率输出,从速度和功率两个闭环入手,建立了以系统辨识为基础的系统模型,并进行了占空比—速度模型和脉宽—功率模型的辨识试验,获取了辨识的输入输出数据,按照选取的模型类及规则确定了模型结构和参数;通过模型输出与实际试验输出数据对比,验证了辨识模型的正确性。通过分析占空比—速度模型和脉宽—功率模型的开环性能,确定了使用RST的控制策略的闭环控制。在确定闭环控制器的性能指标后,利用RST算法设计出控制器,并对控制器的性能进行了仿真和试验研究,结果表明控制器能够达到设计指标,且其稳定性、抗干扰性及跟踪性能均达到要求。考虑到功率闭环中人体疲劳等因素对脉宽功率模型的影响,设计了具有模糊和PID共有特点的模糊PID自校正控制器,仿真和试验结果表明,系统的跟踪性能和抗干扰性能均符合要求。此外,使用模糊T-S控制策略,通过辨识人体不疲劳和疲劳两种状态下的模型,分别设计了两种状态的线性二次型最优控制器和改进RST控制器,将系统两种状态的控制器通过模糊T-S进行融合,使系统能够在不疲劳、疲劳以及中间状态能够自动按照比例输出控制量,仿真结果表明达到设计预期目标。
Functional Electrical Stimulation (FES) is a cross-frontier rehabilitation technology that using electrical impulse via electrodes to stimulate the muscles that are loss of nerve control to contract, thus rebuild their function of related limbs and organs of the body. FES technology has been applied in a variety of areas with various forms. FES cycling system is based on FES technology and integrated with machinery, control and rehabilitation techniques, which can make patients realize the voluntary exercise and assistant walk. Patients may benefit from the exercise for the improvement of cardiorespiratory function, the increase of muscular strength and thickness, and the rebuilding of partial function. Furthermore, it may relieve patients from the family burden and strengthen their confidence to overcome disease. The advantages have been proved by numerous experiments. In the present study, bases on the reality of FES cycling technology, the FES cycling system was constructed and modeled, and the related control strategy was studied. The simulated and experimental results indicate that the system functioned well in the rehabilitation training of patients and achieved the proposed goal.
Firstly, this paper reviews the various techniques of FES cycling system. Based on the present advantages of the cycling system, a FES cycling system based on motor assistance was studied. The characteristics of the system were summarized: it consists of two close loops for control and five structural parts. Constitutions and working principles of every part were introduced in detail with the viewpoint of hardware and software. A dynamic model was built to describe the interaction between the object and the system during the training process. In the model, the kinematical model, electrical stimulation - muscle force model and dynamics model were integrated. Then simulation was conducted under the circumstance of overcoming leg weight. The comparison between the simulated and experimental results verified the accuracy of the model. Based on this analysis, a dynamic model and a simulation model with velocity and pulse width of assistant motor as input data were established to get the muscles output torque. The velocity and pulse width in the identification test and step response test were used in the simulation. The simulation results are in accordance with experimental ones which indicate that the model is with content validity.
Secondly, the system cycling kinematics model was established in this study to get the limit of the angle of related joints in terms of crank angles, which are essential to determine the joint states. Synthesized the effect of muscles contraction to joint state, a zero limit stimulation pattern was obtained to optimize the maximum muscle cadence torque and avoid joints contradictory movement caused by muscle. To testify the experimental optimization method with the aim to obtain the maximum output torque and the minimum muscle force, the optimal objective function was constructed to get the optimization results of initial and terminated angles for three groups of muscles under various crank velocities. With consideration of the delay of the response of simulated muscles, a delay factor was introduced in the model to modify the stimulating patterns. Moreover, the hacking movement was analyzed and its influence factors were determined.
Finally, in order to realize the constant power output of FES cycling system, a systematic identification-based model was developed in the respect of two closed loops, velocity and power. The identifications related to PWM - speed model and the pulse width-power model were tested and the model parameters and the structure in accordance with the selected modal patterns and rules were obtained. The comparison of model output and the actual output data testifies the accuracy of identification model. The RST control strategy of the closed-loop control was used in the study based on the analysis of the open-loop performance of two models. Then, a controller was designed based on the RST algorithm after the determination of the performance specification of the closed loop controller. The simulation and experiments results show that the parameters of controller can satisfy the design requirements, and its stability, anti-disturbance and tracking performance are good. With consideration of the effect of the muscles fatigue on the pulse width - power model, a fuzzy PID auto-adjusted controller was designed with integration of advantages of fussy system and PID. The tracking and anti-disturbance performance of this controller was testified by experimental and simulated results. Besides, a LQR controller and a modified RST controller were designed using fuzzy T-S controlling strategy, based on the identification of the models for the fatigue and non-fatigue states of patients respectively. Whereat, these controllers for different state were integrated through T-S techniques to satisfy the requirements of system so that it can generate output automatically and proportionally when it works at non-fatigue, fatigue states and in-between. The simulated results indicate that the design of the controller is in good use.
引文
1 W. T. Liberson, H. I. Holmquest, D. Scott, and M. Dow. Functional electrotherapy in stimulation of the personal nerve synchronized with the swing phase of the gait of hemiplegic patient . Arch. Phys. Med. Rehab. 1961, 42: 101-107
2 Mulder, A.J., Hermens, H.J., Janssen, F., Zilvold, G.. A low-cost FES exercise bicycle for training paraplegics at home.Journal of Medical Engineering and Technology. 1989,13:90-92
3 J. Rasmussen, S.T. Christensen, M. Gf?hler, M. Damsgard, T. Angeli. Design optimization of a pedaling mechanism for paraplegics. Struct Multidisc Optim. 2004:132-138
4 Chul-Seung Kim,Gwang-Moon Eom,K.Hase. Stimulation Pattern-Free Control of FES Cycling: Simulation Study. Systems Man and Cybernetics Part C: Applications and Reviews.2008,38(1):125-134
5 C.Long, A.Masciarelli. An electrophysiological splint for the hand. Arch. Phys. Med. Rehab. 1963,44:449
6 L.Vodovnic, W.J.Crochetiere, J.B.Reswick, Control of a skeletal joint by electrical stimulation of antagonists. Med.& Biol. Eng. 1967,5:97-109
7 R.H.Nathan. Control strategies in FNS systems for the upper extremities. Critical Reviews in Biomedical Engineering. 1993,2:485-568
8 U.Stanic, A. Trnkoczy. Close-loop positioning of heniplegic patient’s joint by means of function electrical stimulation. IEEE Trans. Biomed. Eng.. 1974, 21:365-371
9 P.H. Peckham, J.T. Mortimer. Restoration of hand function in the Application in Neural Prostheses. Hambrecht, E.T. And Reswith, J.B., Ed., Marcel Dekker, New York, 1997
10 R. Kobetic, J. M. Pereira, E.B. Marsolais. Electromyographic study of the side step for development of electrical stimulation patterns. RESNA '86: Employing Technology, Proceedings of the Ninth Annual Conference on Rehabilitation Technology. Minneapolis, MN, USA. 1986:372-375
11 R. Triolo, R.Nathan, Y.Handa, M.Keith, R.R.Betz,S.Carroll, C.Kantor. Challenges to clinical deployment of upper limb neuroprostheses. JournalRehabilitation Research and Development. 1996,33:111-122
12 T.L. Bennett, R.M. Glaser, T.W.J. Janssen, W.P. Couch, C.J. Herr, J.W. Almeyda, S.H. Petrofsky, P. Akuthota. Development of a hybrid strength training technique for paretic lower-limb muscles. Proceedings of the 1996 15th Southern Biomedical Engineering Conference. Dayton, OH, USA. 1996: 29-31
13 Jia-Jin J. Chen, Hsin-Min Lee,Ying-Zu Huang. Validation of portable muscle tone measurement device based on a motor-driven system. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Istanbul, Turkey. 2001:25-28
14 K.F. Eichhorn, W. Schubert, E. David. Maintenance, training and functional use of denervated muscles. Journal of Biomedical Engineering. 1984, 6:205-211
15 R. M. Glaser, J. A. Gruner, S. D. Feinberg, S. R. Collins, Petrofsky, J. S. Vehicle to permit locomotion via paralyzed leg muscles. Proceedings of the 6th Annual Conference on Rehabilitation Engineering: The Promise of Technology. San Diego, CA, USA.1983:131-133
16 J.S. Petrofsky, J. Smith. Three-wheel cycle ergometer for use by men and women with paralysis. Medical & Biological Engineering & Computing.1992,30:364-369
17 B.J.MacIntosh,N.Baker,R.Mraz. Improving functional magnetic resonance imaging motor studies through simultaneous electromyography recordings. Hum. Brain Mapp. 2007,28:835-845
18 G.Ganesh, D.W.Franklin, R.Gassert, H. Imamizu.Accurate real-time feedback of surface EMG during fMRI. Neurophysiol. 2007,97:912-920
19 A.F.Van Roostelaar,R.Remco,B. M.De Jong.fMRI analysis for motor paradigms using EMG-based designs: A validation study. Human Brain Mapping.2007,(28):1117-1127
20 M. Gfohler, M. Loicht, P. Lugner. Exercise tricycle for paraplegics. Medical & Biological Engineering & Computing.1998,36:118-121
21 M. Gfohler, T. Angeli, T. Eberharter, P. Lugner, W. Mayr, C. Hofer. Test bed with force-measuring crank for static and dynamic investigations on cycling by means of functional electrical stimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2001,9:169-180
22 J.C. Moreno, F.J. Brunetti, J.L. Pons. An autonomous control and monitoring system for a lower limb orthosis: The gait project case. Conference Proceedings- 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004. San Francisco, CA, United States.2004,24:2125-2128
23 J.S. Petrofsky, J. Smith. Three-wheel cycle ergometer for use by men and women with paralysis. Medical & Biological Engineering & Computing.1992,3:364-369
24 Pouran D. Faghri, Hjalmar F. Pompe Van Meerdervort, Roger M. Glaser, Stephen F. Figoni. Electrical stimulation-induced contraction to reduce blood stasis during arhtroplasty. IEEE Transactions on Rehabilitation Engineering.1997,5:92-97
25 W.J. Vankan, J.M. Huyghe, C.C. van Donkelaar,M.R. Drost, J.D. Janssen,A. Huson. Mechanical blood-tissue interaction in contracting muscles: a model study. Journal of Biomechanics.1998,31:401-409
26 Glaser, Roger M., Mary M.Rodgers, Janet A. Mulcare, Deborah L. King, Thomas Mathews, Satyendra C. Gupta. Gait characteristics of individuals with multiple sclerosis before and after a 6-month aerobic training program. Journal of Rehabilitation Research and Development.1999,36:183-188
27 Van Duinen, H.Renken, R.Maurits.Relation between muscle and brain activity during isometric contractions of the first dorsal interosseus muscle. Human Brain Mapping.2008,(29):281-299
28 Margit Gfohler, Peter Lugner. Dynamic simulation of FES-cycling: Influence of individual parameters. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2004,12:398-405
29 Chen Kuohsiang,Chen Shih-Ching,sai Kuen-Horng, Chen Jia-Jin, Jason Yu, Nan-Ying, Huang Mao-Hsiung. An improved design of home cycling system via functional electrical stimulation for paraplegics. International Journal of Industrial Ergonomics.2004,34:223-235
30 Geoffrey M. Graham, Adam T. Thrasher, Milos R. Popovic. The effect of random modulation of functional electrical stimulation parameters on muscle fatigue. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2002,14:38-45
31 R. L. Lieber,B. R.Thompson, A. R. Hargens. ELECTRICAL STIMULATION OF SKELETAL MUSCLE AT 10 Hz. Proceedings of the 37th Annual Conference on Engineering in Medicine and Biology 1984, Volume 26.. LosAngeles, CA, USA.1984:236-238
32 S.Francis, X.Lin, S.Aboushoushah, T. P.White.fMRI analysis of active, passive and electrically stimulated ankle dorsiflexion. NeuroImage. 2009, (44): 469-479
33 Y Shimada, K Sato, M Sato, T Matsunaga, S Chida, A Misawa, S Ando, T Minato, K Iizuka. Comparisons of Energy Expenditure during Gait Using the Walkabout and the Primewalker in Paraplegia K Hatakeyama. 4th Annual Conference of the International Functional Electrical Stimulation Society. Yasunobu Handa ,Tohoku University.1999,4:220-225
34 Prisca C. Eser, Nick de N. Donaldson, Hans Knecht, Edgar Stussi. Influence of different stimulation frequencies on power output and fatigue during FES-cycling in recently injured SCI people. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2003,11:236-240
35 Roger M. Glaser,Carol A. Simsen-Harold, Jerrold S. Petrofsky. Metabolic and cardiopulmonary responses of older wheelchair-dependent and ambulatory patients during locomotion. Ergonomics.1983,16:687-689
36 Jerrold Scott Petrofsky, Roger M. Glaser, Chandler A. Phillips, Alexander R. Lind, Carole. Williams. Evaluation of the amplitude and frequency components of the surface emg as an index of muscle fatigue. Ergonomics.1982,2:213-223
37 J. A. Gruner, R. M.Glaser, S. D. Feinberg, S. R. Collins, N. S. Nussbaum, J. S. Petrofsky. Exercise conditioning system for paralyzed leg muscles. Proceedings of the 6th Annual Conference on Rehabilitation Engineering: The Promise of Technology. San Diego, CA, USA.1983:87-89
38 Petrofsky, Jerrold Scott. New algorithm to control a cycle ergometer using electrical stimulation. Medical and Biological Engineering and Computing.2003,41:18-27
39 M. Gf?hler, T. Angeli. Modeling Of Artificially Activated Muscle and Application to FES Cycling. Journal of Mechanics in Medicine and Biology. 2004, 4(1): 77–92
40 T.A. Perkins, N. Donaldson, N.A. C. Hatcher, I.D. Swain, and D.E. Wood. Control of Leg-Powered Paraplegic Cycling Using Stimulation of the Lumbo-Sacral Anterior Spinal Nerve Roots. IEEE Transaction on Neural Systems and Rehabilitation Engineering. 2002, 10(3):104-108
41 Shuenn-Yuh Lee, Shyh-Chyang Lee, Jia-Jin Chen, Jason. VLSI implementation of implantable wireless power and data transmission micro-stimulator for neuromuscular stimulation. IEICE Transactions on Electronics.2004,E87-c:1062-1068
42 Liang Chih-Kuo, Young Gin-Shu,Chen Jia-Jin ,Jason, Chen Chung-Kai. A microcontroller-based implantable neuromuscular stimulation system with wireless power and data transmission for animal experiments. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an.2003,26:493-501
43 Chen Jia-Jin , Yu Nan-Ying, Huang Ding-Gau, Ann Bao-Ting, Chang Gwo-Ching. Applying fuzzy logic to control cycling movement induced by functional electrical stimulation. IEEE Transactions on Rehabilitation Engineering.1997,5:158-169
44 Ann Bao-Ting, Chen Jia-Jin, Ju Ming-Shaung. Implementation of an adaptive fuzzy controller for FES-cycling system. Proceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago, IL, USA.1997,3:1119-1120
45 K. J. Hunt, T. Schauer, N.-O. Neg?rd, W. Stewart, and M. H. Fraser. A pilot study of lower-limb FES cycling in paraplegia. Proc. 7th Annu. Conf. Int. Functional Electrical Stimulation Soc. Ljubljana, Slovenia. 2002,10(3):753-755
46 H. Gollee. A non-linear approach to modeling and control of electrically stimulated skeletal muscle. A doctor dissertation of Glasgow University. 1998: 1-6
47 K.J.Hunt, B.Stone, N.Negard, T.Schauer, M.H.Fraser, A.J.Cathcar, C.Ferrario, S.A.Ward, S.Grant. Control Strategies for Integration of Electric Motor Assist and Functional Electrical Stimulation in Paraplegic Cycling: Utility for Exercise Testing and Mobile Cycling. IEEE transactions on neural systems and rehabilitation engineering. 2005,12(1):89-101
48 K.J.Hunt,R.-P.Jaime and H.Gollee.Robust Control of Electrically-Stimulation Muscle Using Polynomial H∞Design . Control Engineering Practice.2006,9:313-328
49 A. Thrasher, G.M. Graham, M. R. Popovic.Reducing muscle fatigue due to functional electrical stimulation using random modulation of stimulationparameters. Artif. Organs. 2005,29(1):125-134
50 L.D. Duffell,N. Donaldson,D.J. Newham. Why is the Metabolic Efficiency of FES Cycling Low?.Neural Systems and Rehabilitation Engineering. 2009, 17(3):263-269
51 P. Klein, S. Mattys, M. Rooze. Moment Arm Length Variations of Selected Muscles Acting on Talocrural and Subtalar Joints During Movement: An in vitro study. Biomechan. 1996,29: 21–30
52 R. Riener, T. Edrich. Significance of Passive Elastic Joint Moments in FES. in Proc. 2nd IFESS Conf., Vancouver, B.C., Canada, 1997: 103–106.
53 K. J. Astrom, B. Wittenmark. Computer-controlled systems: theory and design. Tsinghua University Press. 2005: 165~186
54 T.Sinkjaer, M.Haugland, A.Inmann, et al. Biopotentials as command and feedback signals in functional electrical stimulation systems. Medical Engineering & Physics. 2003,25: 29-40
55 C.T.Freeman, A.M.Hughes, J.H.Burridge, et al. Iterative learning control of FES applied to the upper extremity for rehabilitation. Control Engineering Practice. 2009(17): 368-371
56 D.Graupe, K.H.Kohn. A critical review of EMG controlled electrical stimulation in paraplegics. CRC Crit.Rev.Biomed. 1988,15(3):187-210.
57 Zlatko M, Tadej B, Gregoric M, et al. Trunk muscles strength evaluation in normal and paraplegic subject. Proceedings of 9th ICMMB. Ljubljana, Slovenia, 1996: 437-440.
58 Gfhler M, Lugner vienna P. Dynamic simulation of FES-cyclig: infuence of individual parameters. 2005 IEEE Trans Neural System Lehail. 2005,12(47): 398-405
59 M. Alegre, et al: Beta electroencephalograph changes during passive movements: sensory afferences contribute to beta event-related desynchronization in humans, Neuroscience Letters. 2002, 331:29-32.
60 G.R. M¨uller, et al: Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man, Neuroscience Letters. 2003, 340:143-147
61 H. L. Gerrits, A. de Haan, A. J. Sargeant, H. van Langen, and M. T. Hopman,“Peripheral vascular changes after electrically stimulated cycle training in people with spinal cord injury,”Arch. Phys. Med. Rehabil. 2001, 82(6):832–839.
62 P. C. Eser, N. de N. Donaldson, H. Knecht, and E. Stussi,“Influence of different stimulation frequencies on power output and fatigue during FES-cycling in recently injured SCI people,”IEEE Trans. Neural Syst. Rehabil. Eng. 2003, 11(3):236–240.
63 K. J. Hunt, C. Ferrario, S. Grant, B. Stone, A. N. McLean, M. H. Fraser, and D. B. Allan,“Comparison of stimulation patterns for FES-cycling using measures of oxygen cost and stimulation cost,”Med. Eng. Phys. 2006,28(7): 710–718.
64 J. Ding, A. S. Wexler, and S. A. Binder-Macleod,“A predictive fatigue model—II: Predicting the effect of resting times on fatigue,”IEEE Trans.Neural Syst. Rehabil. Eng. 2002, 10(1):59–67.
65 T. W. Janssen, M. Bakker, A. Wyngaert, K. H. Gerrits, and A. de Haan,“Effects of stimulation pattern on electrical stimulation-induced leg cycling performance,”J. Rehabil. Res. Dev. 2004, 41(6):787–796.
66 J. van Soest, M. Gfohler, and L. J. Casius,“Consequences of ankle joint fixation on FES cycling power output: A simulation study,”Med. Sci. Sports Exerc.2005, 37(5):797–806.
67 M. Gfohler and P. Lugner,“Cycling by means of functional electrical stimulation,”IEEE Trans. Rehabil. Eng.2000, 8(2):233–243.
68 Q. T. Tran, D. Docherty, and D. Behm,“The effects of varying time under tension and volume load on acute neuromuscular responses,”Eur. J. Appl Physiol.2006, 98(4): 402–410.
69 N. A. Hakansson and M. L. Hull,“Influence of pedaling rate on muscle mechanical energy in low power recumbent pedaling using forward dynamic simulations,”IEEE Trans. Neural Syst. Rehabil. Eng.2007,15(4):509–516
70 . D. Trumbower and P. D. Faghri,“Kinematic analyses of semireclined leg cycling in able-bodied and spinal cord injured individuals,”Spinal Cord.2005,43(9):543–549.
71 P. J. Sinclair, G. M. Davis, and R. A. Smith,“Musculo-skeletal modeling of NMES-evoked knee extension in spinal cord injury,”J. Biomech. 2006, 39(3): 483–492.
72 P. J. Sinclair, R. M. Smith, and G. M. Davis,“The effect of joint angle on the timing of muscle contractions elicited by neuromuscular electrical stimulation,”IEEE Trans. Neural Syst. Rehabil. Eng.2004, 12(2):303–306.
73 A. Khadilkar, S. Milne, L. Brosseau, V. Robinson, M. Saginur and B. Shea et al., Transcutaneous electrical nerve stimulation (TENS) for chronic low-back pain, Cochrane Database Syst Rev .2005, CD003008.
74 M.R. Prausnitz, S. Mitragotri and R. Langer, Current status and future potential of transdermal drug delivery, Nat Rev Drug Discov. 2004:115–124.
75王磊,罗致诚.神经肌肉功能刺激的肌电闭环控制.中国康复医学杂志.1990,5(3):118-122
76王磊,罗致诚.肌电控制神经肌肉功能电刺激的研究进展.国外医学.1988,11(5):317-321
77王磊,罗致诚.偏瘫患者的健侧对患侧肢体运动的双路肌电闭环控制.中国康复医学杂志.1990,5(4):164-167
78赵光宙,蒋亦农.微机控制截瘫病人步行器的研制.中国医疗器械杂志.1989,13(5):258-263
79赵光宙,蒋亦农:肌电控制截瘫病人步行系统的多级分类器.信号处理.1990,6(1):41-45
80蒋亦农,赵光宙.肌电控制的截瘫病人步行系统.中国康复医学杂志.1990,5(6):249-250,281
81明东,万柏坤,胡勇,梁智仁,用于截瘫FES行走的三位上肢力动态测量系统.实验力学. 2005,20(1):90~91
82鄢达来,周兆英,窦惠芳,张毓笠.FNS中肢体运动的迭代学习控制研究.测试技术学报.1998,12(3):25-30
83毕胜,鄢达来,王福根,周兆英,熊沈蜀.基于肌电信号分析的功能性电刺激控制系统及其应用.中国康复医学杂志.2001,16(1):40-42
84吴怀宇,周兆英,熊沈蜀,鄢达来,章刚华.FNS肢体多关节运动轨迹跟踪的实验研究.中国生物医学工程学报.2001,20(5):394-397
85吴怀宇,周兆英,熊沈蜀,鄢达来,章刚华.迭代学习控制在功能神经电刺激系统中的应用.测控技术.2000,19(7):53-55
86吴怀宇,周兆英,熊沈蜀,鄢达来,章刚华.FNS肢体运动疲劳特性的实验研究.生物医学工程学杂志.2001,18(2):169-172
87毕胜,吴怀宇,章刚华,鄢达来,熊沈蜀,王福根,周兆英.功能性电刺激中应用P型迭代学习控制在上肢多关节运动控制的试验研究.中华理疗杂志.2001,24(2):85-87
88吴怀宇,周兆英,熊沈蜀,方康玲.基于自适应迭代学习算法的FNS肢体运动控制.武汉科技大学学报(自然科学版), 2000,23(04):380-383
89吴怀宇,周兆英,熊沈蜀,方康玲.功能神经肌肉电刺激疲劳特性分析.武汉科技大学学报(自然科学版).2000,23(03):296-298
90朱荣,周兆英,于婷,孙雪峰.肢体运动微型监测系统研究.清华大学学报(自然科学版), 2003,43(11):1464-1467
91王人成,金德闻.仿生智能假肢的研究与进展.中国医疗器械信息.2009,15(1):3-5
92孟凡,贾晓红,王人成,金德闻.基于虚拟游戏的视觉生物反馈技术在康复运动训练中的应用.中国康复医学杂志.2009,24(2):165-166
93胡晓刚,王人成,贾晓红,朱琳,王爱明.表面肌电信号生物反馈治疗系统的研究.中国康复医学杂志.2009,24(3):252-254
94姜明文,王人成,王敬章,金德闻.基于多通道肌电信号小波变换的人手运动识别.中国康复医学杂志.2006,21(1):22-24
95杨建坤,金德闻,王人成,张济川,季林红,方新,周大伟.主元分析在研究滑倒时肌电信号中的应用.中国康复医学杂志.2006,21(1):28-31
96王敬章,李芳,王人成,张邃.人工神经网络在表面肌电信号辨识中的研究进展.中国康复医学杂志.2006,21(1):81-83
97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.生物医学工程学杂志.2006,23(1):69-73
98姜明文,王人成,金德闻,王敬章.握物受扰平衡恢复时上肢运动控制机理的实验研究.航天医学与医学工程.2006,19(2):139-142
99王克义,张立勋,刘攀,张今瑜.绳索牵引骨盆康复机器人的轨迹规划.中国机械工程.2009,20(3):253-257
100张晓超,张立勋.基于模糊自整定PID的下肢康复训练步态机器人变负载控制.测控技术.2009,43-46
101隋立明,张立勋.气动肌肉的强度与可靠性分析.工程设计学报. 2009, 16(1):12-15
102张立勋,张晓超.下肢康复训练机器人步态规划及运动学仿真.哈尔滨工程大学学报.2009,30(2):187-191
103王克义,张立勋,张今瑜.1R2T绳索牵引骨盆康复机器人动力学.江苏大学学报(自然科学版).2010,31(2):131-135
104张立勋,王令军,王凤良.一种人体步态轨迹测量方法.测控技术.2009,28(2):24-27
105姜洪源;,耿建辉,敖宏瑞,胡秀丽.鲁棒PID控制策略在FES脚踏车控制系统中的应用及仿真研究.中国生物医学工程学报.2005,24(2):217-221
106姜洪源,廖志伟,魏伟,李娜.功能性电刺激脚踏车系统在康复医疗中的研究与应用中国临床康复.2006,10(9):153-157
107姜洪源,魏伟,廖志伟,岳伟.FES脚踏车座椅的减振优化设计.医疗装备. 2006,19(6):9-12
108 G.Nemethand H.Ohlsen,. In vivo moment arm lengths for hip extensor muscles at different angles of hip flexion. J. Biomechan. 1985,18(1): 129-140
109 R. Riener, J. Quintern, and G. Schmidt, Biomechanical Model of the Human Knee Evaluated by Neuromuscular Stimulation. Biomechan., 1996.29: 1157–1167,.
110 H. Hatze. A General Myocybernetic Control Model of Skeletal Muscle. Biol. Cybern., 1978.28: 143–157
111 R. Happee. Inverse dynamic optimization including muscular dynamics a new simulation method applied to goal directed movements. Biomechan., 1994.27: 953–960
112 Zadeg, L. A. From Circuit Theory to System Theory. Proc. IRE, 1962, 50(5):856-865
113 Ljung L. System Identification. Theory for the User.Prentice Hall. Englewood Cliffs, NJ, 1999
114李冬伟,白鸿伯,齐晓慧.机械振动结构的ARMAX建模及模态参数识别.机械科学与技术. 2005,24(12):1515-1518
115曹菁,朱纪红.基于零极点配置的伺服控制器设计.电气传动.2006, 36(1): 48-51
116汤伟,王樨,王孟效.一种基于主导极点和相角裕度的PID参数整定方法.化工自动化及仪表.2006,33(6:55-58)
117 LI Ping,JIN Fu-Jiang.Adaptive Fuzzy Control for Unknown Nonlinear Systems with Perturbed Dead-zone Inputs.Acta Automatica Sinica. 2010, 36(4):573-579
118赵阳,邢继峰.潜艇垂直面运动自适应神经网络模糊控制仿真.计算机仿真.2009,26(6):26-29
119陈志盛,孙克辉,李勇刚,张泰山.基于模糊Lyapunov函数的离散模糊时滞系统H∞.控制与决策.2006,21(5):546-549,558
120余正元,胡良剑.随机T-S模糊系统均方镇定的参数化LMI方法.东华大学学报(自然科学版).2007,33(06):717-721
2 Mulder, A.J., Hermens, H.J., Janssen, F., Zilvold, G.. A low-cost FES exercise bicycle for training paraplegics at home.Journal of Medical Engineering and Technology. 1989,13:90-92
3 J. Rasmussen, S.T. Christensen, M. Gf?hler, M. Damsgard, T. Angeli. Design optimization of a pedaling mechanism for paraplegics. Struct Multidisc Optim. 2004:132-138
4 Chul-Seung Kim,Gwang-Moon Eom,K.Hase. Stimulation Pattern-Free Control of FES Cycling: Simulation Study. Systems Man and Cybernetics Part C: Applications and Reviews.2008,38(1):125-134
5 C.Long, A.Masciarelli. An electrophysiological splint for the hand. Arch. Phys. Med. Rehab. 1963,44:449
6 L.Vodovnic, W.J.Crochetiere, J.B.Reswick, Control of a skeletal joint by electrical stimulation of antagonists. Med.& Biol. Eng. 1967,5:97-109
7 R.H.Nathan. Control strategies in FNS systems for the upper extremities. Critical Reviews in Biomedical Engineering. 1993,2:485-568
8 U.Stanic, A. Trnkoczy. Close-loop positioning of heniplegic patient’s joint by means of function electrical stimulation. IEEE Trans. Biomed. Eng.. 1974, 21:365-371
9 P.H. Peckham, J.T. Mortimer. Restoration of hand function in the Application in Neural Prostheses. Hambrecht, E.T. And Reswith, J.B., Ed., Marcel Dekker, New York, 1997
10 R. Kobetic, J. M. Pereira, E.B. Marsolais. Electromyographic study of the side step for development of electrical stimulation patterns. RESNA '86: Employing Technology, Proceedings of the Ninth Annual Conference on Rehabilitation Technology. Minneapolis, MN, USA. 1986:372-375
11 R. Triolo, R.Nathan, Y.Handa, M.Keith, R.R.Betz,S.Carroll, C.Kantor. Challenges to clinical deployment of upper limb neuroprostheses. JournalRehabilitation Research and Development. 1996,33:111-122
12 T.L. Bennett, R.M. Glaser, T.W.J. Janssen, W.P. Couch, C.J. Herr, J.W. Almeyda, S.H. Petrofsky, P. Akuthota. Development of a hybrid strength training technique for paretic lower-limb muscles. Proceedings of the 1996 15th Southern Biomedical Engineering Conference. Dayton, OH, USA. 1996: 29-31
13 Jia-Jin J. Chen, Hsin-Min Lee,Ying-Zu Huang. Validation of portable muscle tone measurement device based on a motor-driven system. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Istanbul, Turkey. 2001:25-28
14 K.F. Eichhorn, W. Schubert, E. David. Maintenance, training and functional use of denervated muscles. Journal of Biomedical Engineering. 1984, 6:205-211
15 R. M. Glaser, J. A. Gruner, S. D. Feinberg, S. R. Collins, Petrofsky, J. S. Vehicle to permit locomotion via paralyzed leg muscles. Proceedings of the 6th Annual Conference on Rehabilitation Engineering: The Promise of Technology. San Diego, CA, USA.1983:131-133
16 J.S. Petrofsky, J. Smith. Three-wheel cycle ergometer for use by men and women with paralysis. Medical & Biological Engineering & Computing.1992,30:364-369
17 B.J.MacIntosh,N.Baker,R.Mraz. Improving functional magnetic resonance imaging motor studies through simultaneous electromyography recordings. Hum. Brain Mapp. 2007,28:835-845
18 G.Ganesh, D.W.Franklin, R.Gassert, H. Imamizu.Accurate real-time feedback of surface EMG during fMRI. Neurophysiol. 2007,97:912-920
19 A.F.Van Roostelaar,R.Remco,B. M.De Jong.fMRI analysis for motor paradigms using EMG-based designs: A validation study. Human Brain Mapping.2007,(28):1117-1127
20 M. Gfohler, M. Loicht, P. Lugner. Exercise tricycle for paraplegics. Medical & Biological Engineering & Computing.1998,36:118-121
21 M. Gfohler, T. Angeli, T. Eberharter, P. Lugner, W. Mayr, C. Hofer. Test bed with force-measuring crank for static and dynamic investigations on cycling by means of functional electrical stimulation. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2001,9:169-180
22 J.C. Moreno, F.J. Brunetti, J.L. Pons. An autonomous control and monitoring system for a lower limb orthosis: The gait project case. Conference Proceedings- 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004. San Francisco, CA, United States.2004,24:2125-2128
23 J.S. Petrofsky, J. Smith. Three-wheel cycle ergometer for use by men and women with paralysis. Medical & Biological Engineering & Computing.1992,3:364-369
24 Pouran D. Faghri, Hjalmar F. Pompe Van Meerdervort, Roger M. Glaser, Stephen F. Figoni. Electrical stimulation-induced contraction to reduce blood stasis during arhtroplasty. IEEE Transactions on Rehabilitation Engineering.1997,5:92-97
25 W.J. Vankan, J.M. Huyghe, C.C. van Donkelaar,M.R. Drost, J.D. Janssen,A. Huson. Mechanical blood-tissue interaction in contracting muscles: a model study. Journal of Biomechanics.1998,31:401-409
26 Glaser, Roger M., Mary M.Rodgers, Janet A. Mulcare, Deborah L. King, Thomas Mathews, Satyendra C. Gupta. Gait characteristics of individuals with multiple sclerosis before and after a 6-month aerobic training program. Journal of Rehabilitation Research and Development.1999,36:183-188
27 Van Duinen, H.Renken, R.Maurits.Relation between muscle and brain activity during isometric contractions of the first dorsal interosseus muscle. Human Brain Mapping.2008,(29):281-299
28 Margit Gfohler, Peter Lugner. Dynamic simulation of FES-cycling: Influence of individual parameters. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2004,12:398-405
29 Chen Kuohsiang,Chen Shih-Ching,sai Kuen-Horng, Chen Jia-Jin, Jason Yu, Nan-Ying, Huang Mao-Hsiung. An improved design of home cycling system via functional electrical stimulation for paraplegics. International Journal of Industrial Ergonomics.2004,34:223-235
30 Geoffrey M. Graham, Adam T. Thrasher, Milos R. Popovic. The effect of random modulation of functional electrical stimulation parameters on muscle fatigue. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2002,14:38-45
31 R. L. Lieber,B. R.Thompson, A. R. Hargens. ELECTRICAL STIMULATION OF SKELETAL MUSCLE AT 10 Hz. Proceedings of the 37th Annual Conference on Engineering in Medicine and Biology 1984, Volume 26.. LosAngeles, CA, USA.1984:236-238
32 S.Francis, X.Lin, S.Aboushoushah, T. P.White.fMRI analysis of active, passive and electrically stimulated ankle dorsiflexion. NeuroImage. 2009, (44): 469-479
33 Y Shimada, K Sato, M Sato, T Matsunaga, S Chida, A Misawa, S Ando, T Minato, K Iizuka. Comparisons of Energy Expenditure during Gait Using the Walkabout and the Primewalker in Paraplegia K Hatakeyama. 4th Annual Conference of the International Functional Electrical Stimulation Society. Yasunobu Handa ,Tohoku University.1999,4:220-225
34 Prisca C. Eser, Nick de N. Donaldson, Hans Knecht, Edgar Stussi. Influence of different stimulation frequencies on power output and fatigue during FES-cycling in recently injured SCI people. IEEE Transactions on Neural Systems and Rehabilitation Engineering.2003,11:236-240
35 Roger M. Glaser,Carol A. Simsen-Harold, Jerrold S. Petrofsky. Metabolic and cardiopulmonary responses of older wheelchair-dependent and ambulatory patients during locomotion. Ergonomics.1983,16:687-689
36 Jerrold Scott Petrofsky, Roger M. Glaser, Chandler A. Phillips, Alexander R. Lind, Carole. Williams. Evaluation of the amplitude and frequency components of the surface emg as an index of muscle fatigue. Ergonomics.1982,2:213-223
37 J. A. Gruner, R. M.Glaser, S. D. Feinberg, S. R. Collins, N. S. Nussbaum, J. S. Petrofsky. Exercise conditioning system for paralyzed leg muscles. Proceedings of the 6th Annual Conference on Rehabilitation Engineering: The Promise of Technology. San Diego, CA, USA.1983:87-89
38 Petrofsky, Jerrold Scott. New algorithm to control a cycle ergometer using electrical stimulation. Medical and Biological Engineering and Computing.2003,41:18-27
39 M. Gf?hler, T. Angeli. Modeling Of Artificially Activated Muscle and Application to FES Cycling. Journal of Mechanics in Medicine and Biology. 2004, 4(1): 77–92
40 T.A. Perkins, N. Donaldson, N.A. C. Hatcher, I.D. Swain, and D.E. Wood. Control of Leg-Powered Paraplegic Cycling Using Stimulation of the Lumbo-Sacral Anterior Spinal Nerve Roots. IEEE Transaction on Neural Systems and Rehabilitation Engineering. 2002, 10(3):104-108
41 Shuenn-Yuh Lee, Shyh-Chyang Lee, Jia-Jin Chen, Jason. VLSI implementation of implantable wireless power and data transmission micro-stimulator for neuromuscular stimulation. IEICE Transactions on Electronics.2004,E87-c:1062-1068
42 Liang Chih-Kuo, Young Gin-Shu,Chen Jia-Jin ,Jason, Chen Chung-Kai. A microcontroller-based implantable neuromuscular stimulation system with wireless power and data transmission for animal experiments. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an.2003,26:493-501
43 Chen Jia-Jin , Yu Nan-Ying, Huang Ding-Gau, Ann Bao-Ting, Chang Gwo-Ching. Applying fuzzy logic to control cycling movement induced by functional electrical stimulation. IEEE Transactions on Rehabilitation Engineering.1997,5:158-169
44 Ann Bao-Ting, Chen Jia-Jin, Ju Ming-Shaung. Implementation of an adaptive fuzzy controller for FES-cycling system. Proceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago, IL, USA.1997,3:1119-1120
45 K. J. Hunt, T. Schauer, N.-O. Neg?rd, W. Stewart, and M. H. Fraser. A pilot study of lower-limb FES cycling in paraplegia. Proc. 7th Annu. Conf. Int. Functional Electrical Stimulation Soc. Ljubljana, Slovenia. 2002,10(3):753-755
46 H. Gollee. A non-linear approach to modeling and control of electrically stimulated skeletal muscle. A doctor dissertation of Glasgow University. 1998: 1-6
47 K.J.Hunt, B.Stone, N.Negard, T.Schauer, M.H.Fraser, A.J.Cathcar, C.Ferrario, S.A.Ward, S.Grant. Control Strategies for Integration of Electric Motor Assist and Functional Electrical Stimulation in Paraplegic Cycling: Utility for Exercise Testing and Mobile Cycling. IEEE transactions on neural systems and rehabilitation engineering. 2005,12(1):89-101
48 K.J.Hunt,R.-P.Jaime and H.Gollee.Robust Control of Electrically-Stimulation Muscle Using Polynomial H∞Design . Control Engineering Practice.2006,9:313-328
49 A. Thrasher, G.M. Graham, M. R. Popovic.Reducing muscle fatigue due to functional electrical stimulation using random modulation of stimulationparameters. Artif. Organs. 2005,29(1):125-134
50 L.D. Duffell,N. Donaldson,D.J. Newham. Why is the Metabolic Efficiency of FES Cycling Low?.Neural Systems and Rehabilitation Engineering. 2009, 17(3):263-269
51 P. Klein, S. Mattys, M. Rooze. Moment Arm Length Variations of Selected Muscles Acting on Talocrural and Subtalar Joints During Movement: An in vitro study. Biomechan. 1996,29: 21–30
52 R. Riener, T. Edrich. Significance of Passive Elastic Joint Moments in FES. in Proc. 2nd IFESS Conf., Vancouver, B.C., Canada, 1997: 103–106.
53 K. J. Astrom, B. Wittenmark. Computer-controlled systems: theory and design. Tsinghua University Press. 2005: 165~186
54 T.Sinkjaer, M.Haugland, A.Inmann, et al. Biopotentials as command and feedback signals in functional electrical stimulation systems. Medical Engineering & Physics. 2003,25: 29-40
55 C.T.Freeman, A.M.Hughes, J.H.Burridge, et al. Iterative learning control of FES applied to the upper extremity for rehabilitation. Control Engineering Practice. 2009(17): 368-371
56 D.Graupe, K.H.Kohn. A critical review of EMG controlled electrical stimulation in paraplegics. CRC Crit.Rev.Biomed. 1988,15(3):187-210.
57 Zlatko M, Tadej B, Gregoric M, et al. Trunk muscles strength evaluation in normal and paraplegic subject. Proceedings of 9th ICMMB. Ljubljana, Slovenia, 1996: 437-440.
58 Gfhler M, Lugner vienna P. Dynamic simulation of FES-cyclig: infuence of individual parameters. 2005 IEEE Trans Neural System Lehail. 2005,12(47): 398-405
59 M. Alegre, et al: Beta electroencephalograph changes during passive movements: sensory afferences contribute to beta event-related desynchronization in humans, Neuroscience Letters. 2002, 331:29-32.
60 G.R. M¨uller, et al: Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man, Neuroscience Letters. 2003, 340:143-147
61 H. L. Gerrits, A. de Haan, A. J. Sargeant, H. van Langen, and M. T. Hopman,“Peripheral vascular changes after electrically stimulated cycle training in people with spinal cord injury,”Arch. Phys. Med. Rehabil. 2001, 82(6):832–839.
62 P. C. Eser, N. de N. Donaldson, H. Knecht, and E. Stussi,“Influence of different stimulation frequencies on power output and fatigue during FES-cycling in recently injured SCI people,”IEEE Trans. Neural Syst. Rehabil. Eng. 2003, 11(3):236–240.
63 K. J. Hunt, C. Ferrario, S. Grant, B. Stone, A. N. McLean, M. H. Fraser, and D. B. Allan,“Comparison of stimulation patterns for FES-cycling using measures of oxygen cost and stimulation cost,”Med. Eng. Phys. 2006,28(7): 710–718.
64 J. Ding, A. S. Wexler, and S. A. Binder-Macleod,“A predictive fatigue model—II: Predicting the effect of resting times on fatigue,”IEEE Trans.Neural Syst. Rehabil. Eng. 2002, 10(1):59–67.
65 T. W. Janssen, M. Bakker, A. Wyngaert, K. H. Gerrits, and A. de Haan,“Effects of stimulation pattern on electrical stimulation-induced leg cycling performance,”J. Rehabil. Res. Dev. 2004, 41(6):787–796.
66 J. van Soest, M. Gfohler, and L. J. Casius,“Consequences of ankle joint fixation on FES cycling power output: A simulation study,”Med. Sci. Sports Exerc.2005, 37(5):797–806.
67 M. Gfohler and P. Lugner,“Cycling by means of functional electrical stimulation,”IEEE Trans. Rehabil. Eng.2000, 8(2):233–243.
68 Q. T. Tran, D. Docherty, and D. Behm,“The effects of varying time under tension and volume load on acute neuromuscular responses,”Eur. J. Appl Physiol.2006, 98(4): 402–410.
69 N. A. Hakansson and M. L. Hull,“Influence of pedaling rate on muscle mechanical energy in low power recumbent pedaling using forward dynamic simulations,”IEEE Trans. Neural Syst. Rehabil. Eng.2007,15(4):509–516
70 . D. Trumbower and P. D. Faghri,“Kinematic analyses of semireclined leg cycling in able-bodied and spinal cord injured individuals,”Spinal Cord.2005,43(9):543–549.
71 P. J. Sinclair, G. M. Davis, and R. A. Smith,“Musculo-skeletal modeling of NMES-evoked knee extension in spinal cord injury,”J. Biomech. 2006, 39(3): 483–492.
72 P. J. Sinclair, R. M. Smith, and G. M. Davis,“The effect of joint angle on the timing of muscle contractions elicited by neuromuscular electrical stimulation,”IEEE Trans. Neural Syst. Rehabil. Eng.2004, 12(2):303–306.
73 A. Khadilkar, S. Milne, L. Brosseau, V. Robinson, M. Saginur and B. Shea et al., Transcutaneous electrical nerve stimulation (TENS) for chronic low-back pain, Cochrane Database Syst Rev .2005, CD003008.
74 M.R. Prausnitz, S. Mitragotri and R. Langer, Current status and future potential of transdermal drug delivery, Nat Rev Drug Discov. 2004:115–124.
75王磊,罗致诚.神经肌肉功能刺激的肌电闭环控制.中国康复医学杂志.1990,5(3):118-122
76王磊,罗致诚.肌电控制神经肌肉功能电刺激的研究进展.国外医学.1988,11(5):317-321
77王磊,罗致诚.偏瘫患者的健侧对患侧肢体运动的双路肌电闭环控制.中国康复医学杂志.1990,5(4):164-167
78赵光宙,蒋亦农.微机控制截瘫病人步行器的研制.中国医疗器械杂志.1989,13(5):258-263
79赵光宙,蒋亦农:肌电控制截瘫病人步行系统的多级分类器.信号处理.1990,6(1):41-45
80蒋亦农,赵光宙.肌电控制的截瘫病人步行系统.中国康复医学杂志.1990,5(6):249-250,281
81明东,万柏坤,胡勇,梁智仁,用于截瘫FES行走的三位上肢力动态测量系统.实验力学. 2005,20(1):90~91
82鄢达来,周兆英,窦惠芳,张毓笠.FNS中肢体运动的迭代学习控制研究.测试技术学报.1998,12(3):25-30
83毕胜,鄢达来,王福根,周兆英,熊沈蜀.基于肌电信号分析的功能性电刺激控制系统及其应用.中国康复医学杂志.2001,16(1):40-42
84吴怀宇,周兆英,熊沈蜀,鄢达来,章刚华.FNS肢体多关节运动轨迹跟踪的实验研究.中国生物医学工程学报.2001,20(5):394-397
85吴怀宇,周兆英,熊沈蜀,鄢达来,章刚华.迭代学习控制在功能神经电刺激系统中的应用.测控技术.2000,19(7):53-55
86吴怀宇,周兆英,熊沈蜀,鄢达来,章刚华.FNS肢体运动疲劳特性的实验研究.生物医学工程学杂志.2001,18(2):169-172
87毕胜,吴怀宇,章刚华,鄢达来,熊沈蜀,王福根,周兆英.功能性电刺激中应用P型迭代学习控制在上肢多关节运动控制的试验研究.中华理疗杂志.2001,24(2):85-87
88吴怀宇,周兆英,熊沈蜀,方康玲.基于自适应迭代学习算法的FNS肢体运动控制.武汉科技大学学报(自然科学版), 2000,23(04):380-383
89吴怀宇,周兆英,熊沈蜀,方康玲.功能神经肌肉电刺激疲劳特性分析.武汉科技大学学报(自然科学版).2000,23(03):296-298
90朱荣,周兆英,于婷,孙雪峰.肢体运动微型监测系统研究.清华大学学报(自然科学版), 2003,43(11):1464-1467
91王人成,金德闻.仿生智能假肢的研究与进展.中国医疗器械信息.2009,15(1):3-5
92孟凡,贾晓红,王人成,金德闻.基于虚拟游戏的视觉生物反馈技术在康复运动训练中的应用.中国康复医学杂志.2009,24(2):165-166
93胡晓刚,王人成,贾晓红,朱琳,王爱明.表面肌电信号生物反馈治疗系统的研究.中国康复医学杂志.2009,24(3):252-254
94姜明文,王人成,王敬章,金德闻.基于多通道肌电信号小波变换的人手运动识别.中国康复医学杂志.2006,21(1):22-24
95杨建坤,金德闻,王人成,张济川,季林红,方新,周大伟.主元分析在研究滑倒时肌电信号中的应用.中国康复医学杂志.2006,21(1):28-31
96王敬章,李芳,王人成,张邃.人工神经网络在表面肌电信号辨识中的研究进展.中国康复医学杂志.2006,21(1):81-83
97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.生物医学工程学杂志.2006,23(1):69-73
98姜明文,王人成,金德闻,王敬章.握物受扰平衡恢复时上肢运动控制机理的实验研究.航天医学与医学工程.2006,19(2):139-142
99王克义,张立勋,刘攀,张今瑜.绳索牵引骨盆康复机器人的轨迹规划.中国机械工程.2009,20(3):253-257
100张晓超,张立勋.基于模糊自整定PID的下肢康复训练步态机器人变负载控制.测控技术.2009,43-46
101隋立明,张立勋.气动肌肉的强度与可靠性分析.工程设计学报. 2009, 16(1):12-15
102张立勋,张晓超.下肢康复训练机器人步态规划及运动学仿真.哈尔滨工程大学学报.2009,30(2):187-191
103王克义,张立勋,张今瑜.1R2T绳索牵引骨盆康复机器人动力学.江苏大学学报(自然科学版).2010,31(2):131-135
104张立勋,王令军,王凤良.一种人体步态轨迹测量方法.测控技术.2009,28(2):24-27
105姜洪源;,耿建辉,敖宏瑞,胡秀丽.鲁棒PID控制策略在FES脚踏车控制系统中的应用及仿真研究.中国生物医学工程学报.2005,24(2):217-221
106姜洪源,廖志伟,魏伟,李娜.功能性电刺激脚踏车系统在康复医疗中的研究与应用中国临床康复.2006,10(9):153-157
107姜洪源,魏伟,廖志伟,岳伟.FES脚踏车座椅的减振优化设计.医疗装备. 2006,19(6):9-12
108 G.Nemethand H.Ohlsen,. In vivo moment arm lengths for hip extensor muscles at different angles of hip flexion. J. Biomechan. 1985,18(1): 129-140
109 R. Riener, J. Quintern, and G. Schmidt, Biomechanical Model of the Human Knee Evaluated by Neuromuscular Stimulation. Biomechan., 1996.29: 1157–1167,.
110 H. Hatze. A General Myocybernetic Control Model of Skeletal Muscle. Biol. Cybern., 1978.28: 143–157
111 R. Happee. Inverse dynamic optimization including muscular dynamics a new simulation method applied to goal directed movements. Biomechan., 1994.27: 953–960
112 Zadeg, L. A. From Circuit Theory to System Theory. Proc. IRE, 1962, 50(5):856-865
113 Ljung L. System Identification. Theory for the User.Prentice Hall. Englewood Cliffs, NJ, 1999
114李冬伟,白鸿伯,齐晓慧.机械振动结构的ARMAX建模及模态参数识别.机械科学与技术. 2005,24(12):1515-1518
115曹菁,朱纪红.基于零极点配置的伺服控制器设计.电气传动.2006, 36(1): 48-51
116汤伟,王樨,王孟效.一种基于主导极点和相角裕度的PID参数整定方法.化工自动化及仪表.2006,33(6:55-58)
117 LI Ping,JIN Fu-Jiang.Adaptive Fuzzy Control for Unknown Nonlinear Systems with Perturbed Dead-zone Inputs.Acta Automatica Sinica. 2010, 36(4):573-579
118赵阳,邢继峰.潜艇垂直面运动自适应神经网络模糊控制仿真.计算机仿真.2009,26(6):26-29
119陈志盛,孙克辉,李勇刚,张泰山.基于模糊Lyapunov函数的离散模糊时滞系统H∞.控制与决策.2006,21(5):546-549,558
120余正元,胡良剑.随机T-S模糊系统均方镇定的参数化LMI方法.东华大学学报(自然科学版).2007,33(06):717-721
© 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 |