Prediction Model for Dynamic Joint Torque of Lower Limb with Surface EMG

doi:10.7652/xjtuxb201512005

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Prediction Model for Dynamic Joint Torque of Lower Limb with Surface EMG

更新时间：2025-07-09

- Prediction Model for Dynamic Joint Torque of Lower Limb with Surface EMG
- Vol. 49, Issue 12, Pages: 26-33(2015)
- 作者机构：
  
  西安交通大学现代设计及转子轴承系统教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201512005
  CLC： TP242
- Online First：10 December 2015，
  
  Published：2015
- 稿件说明：
移动端阅览
Prediction Model for Dynamic Joint Torque of Lower Limb with Surface EMG[J]. 2015, 49(12): 26-33.
DOI：

Prediction Model for Dynamic Joint Torque of Lower Limb with Surface EMG[J]. 2015, 49(12): 26-33. DOI： 10.7652/xjtuxb201512005.

摘要

为实现表面肌电信号的下肢关节力矩动态解码

建立了从表面肌电信号到关节力矩输出的人体下肢运动系统正向生物力学模型。首先

从幅值和频率两个角度建立表面肌电信号到骨骼肌激活程度模型; 其次

根据肌丝滑移理论

构建反映骨骼肌生理结构和微观力学特性的肌肉力模型

同时确定活动肌肉拉力线方向及力作用点位移矢量

将骨骼肌力转换到关节力矩; 最后

以牛顿-欧拉逆动力学方法获得关节力矩作为准确值

给出正向生物力学模型参数动态标定方法。在模型基础上

对4名对象进行随意步态下膝关节屈伸动态力矩预测试验

结果表明:所建模型对步态行走下的膝关节动态关节力矩具有很好的动态跟踪性能

最大绝对误差为(11.0±1.32)N·m

平均残差为(4.43±0.698)N·m

预测值与准确值之间的平均线性相关系数为0.927±0.042

验证了该方法的正确性和有效性; 可为康复训练机器人人机协同过程中的力学交互模式研究提供接口。

Abstract

To achieve the dynamic joint torque decoding from surface electromyography(EMG)

the forward biomechanical model of lower limb motor system

which relates the surface EMG and joint torque

is established. The dynamic surface EMG to skeletal muscle activation model is constructed from the perspective of amplitude and frequency. Then the muscle contraction model reflecting physiological structure and micromechanical properties is constructed according to the sliding-filament theory. The force direction and displacement vector of active muscle are determined and the transformation from muscle force to joint moment is realized. The dynamic calibration for the forward biomechanical model using the exact joint torque value obtained with Newton-Eular method is finally put forward. Following the calibration

the flexion/extension(FE)knee joint torque of four objects under different speed walking is predicted. The results show that the forward biomechanical model can capture the general shape and timing of the joint torque

the maximum absolute error is(11.0±1.32)N·m

the mean residual error is(4.43±0.698)N·m

and the linear relationship between predicted and exact knee FE torque reaches 0.927±0.042. This prediction model provides an interface for the study of force interaction pattern in the process of human-machine cooperation in training.

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references

DÍAZ I, GIL J J, SÁNCHEZ E. Lower-limb robotic rehabilitation: literature review and challenges [J]. Journal of Robotics, 2011, 2011: 759764.

LUM P, REINKENSMEYER D, MAHONEY R, et al. Robotic devices for movement therapy after stroke: current status and challenges to clinical acceptance [J]. Topics in Stroke Rehabilitation, 2002, 8(4): 40-53.

EKKELENKAMP R, VENEMAN J, VANDER KOOIJ H. LOPES: selective control of gait functions during the gait rehabilitation of CVA patients [C]∥9th International Conference on Rehabilitation Robotics. Piscataway, NJ, USA: IEEE, 2005: 361-364.

文忠, 钱晋武, 沈林勇, 等. 基于阻抗控制的步行康复训练机器人的轨迹自适应 [J]. 机器人, 2011, 33(1): 142-149.

WEN Zhong, QIAN Jinwu, SHEN Linyong, et al. Trajectory adaptation for impedance control based walking rehabilitation training robot [J]. Robot, 2011, 33(1): 142-149.

HAYASHI T, KAWAMOTO H, SANKAI Y. Control method of robot suit HAL working as operator's muscle using biological and dynamical information [C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2005: 3063-3068.

FLEISCHER C, HOMMEL G. A human-exoskeleton interface utilizing electromyography [J]. IEEE Transactions on Robotics, 2008, 24(4): 872-882.

KREBS H I, PALAZZOLO J J, DIPIETRO L, et al. Rehabilitation robotics: performance based progressive robot-assisted therapy [J]. Autonomous Robots, 2003, 15(1): 7-20.

PERRY J C, ROSEN J, BURNS S. Upper-limb powered exoskeleton design [J]. IEEE/ASME Transactions on Mechatronics, 2007, 12(4): 408-417.

KREBS H, VOLPE B, WILLIAMS D, et al. Robot-aided neurorehabilitation: a robot for wrist rehabilitation [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15(3): 327-335.

MORITA S, KONDO T, ITO K. Estimation of forearm movement from EMG signal and application to prosthetic hand control [C]∥International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2001: 3692-3697.

LLOYD D G, BESIER T F. An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo [J]. Journal of Biomechanics, 2003, 36(6): 765-776.

BUCHANAN T S, LLOYD D G, MANAL K, et al. Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command [J]. Journal of Applied Biomechanics, 2004, 20(4): 367-395.

HILL A V. The heat of shortening and the dynamic constants of muscle [J]. Proceedings of the Royal Society of London: Series B Biological Sciences, 1938, 126(843): 136-195.

GUO Zhao, FAN Yuanjie, ZHANG Jianjun, et al. A new 4M model-based human-machine interface for lower extremity exoskeleton robot [C]∥The 5th International Conference on Intelligent Robotics and Applications. Montreal, Canada: ICIRA, 2012: 545-554.

YIN Y H, CHEN X. Bioelectrochemical control mechanism with variable-frequency regulation for skeletal muscle contraction-biomechanics of skeletal muscle based on the working mechanism of myosin motors(II)[J]. Science China: Technological Sciences, 2012, 55: 2115-2125.

WANG W, DE STEFANO A, ALLEN R. A simulation model of the surface EMG signal for analysis of muscle activity during the gait cycle [J]. Computers in Biology and Medicine, 2006, 36(6): 601-618.[17] HUXLEY A F, SIMMONS R M. Proposed mechanism of force generation in striated muscle [J]. Nature, 1971, 233(5321): 533-538.

JULICHER F, AJDARI A, PROST J. Modeling molecular motors [J]. Reviews of Modern Physics, 1997, 69(4): 1269-1281.

LECARPENTIER Y, BLANC F X, QUILLARD J, et al. Statistical mechanics of myosin molecular motors in skeletal muscles [J]. Journal of Theoretical Biology, 2005, 235(3): 381-392.

YIN Yuehong, GUO Zhao. Collective mechanism of molecular motors and a dynamic mechanical model for sarcomere [J]. Science China: Technological Sciences, 2011, 54(8): 2130-2137.

GOLLAPUDI S K, LIN D C. Experimental determination of sarcomere force-length relationship in type-I human skeletal muscle fibers [J]. Journal of Biomechanics, 2009, 42(13): 2011-2016.

单大卯. 人体下肢肌肉功能模型及其应用的研究 [D]. 上海: 上海体育学院, 2003.

CAMOMILLA V, CEREATTI A, VANNOZZI G, et al. An optimized protocol for hip joint center determination using the functional method [J]. Journal of Biomechanics, 2006, 39(6): 1096-1106.

HARRINGTON M E, ZAVATSKY A B, LAWSON S E, et al. Prediction of the hip joint centre in adults, children, and patients with cerebral palsy based on magnetic resonance imaging [J]. Journal of Biomechanics, 2007, 40(3): 595-602.

VAUGHAN C L, DAVIS B L, O'CONNOR J C. Dynamics of human gait [M]. Champaign, Illinois, USA: Human Kinetics Publishers, 1992: 83-106.

ARNOLD E M, WARD S R, LIEBER R L, et al. A model of the lower limb for analysis of human movement [J]. Annals of Biomedical Engineering, 2010, 38(2): 269-279.

HOWATSON G, GLAISTER M, BROUNER J, et al. The reliability of electromechanical delay and torque during isometric and concentric isokinetic contractions [J]. Journal of Electromyography and Kinesiology, 2009, 19(5): 975-979.

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