速度场与意图角双重变导纳主动康复训练策略研究

李辽远; 韩建海; 李向攀; 郭冰菁; 杜敢琴; 王新杰

doi:10.7652/xjtuxb202307002

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速度场与意图角双重变导纳主动康复训练策略研究

专题医工交叉与人机交互 | 更新时间：2025-07-09

- 速度场与意图角双重变导纳主动康复训练策略研究
- Research on Active Rehabilitation Training Strategy Based on Dual Variable Admittance of Velocity Field and Intention Angle
- 西安交通大学学报 2023年57卷第7期页码：9-19
- 作者机构：
  
  1. 河南科技大学机电工程学院,河南,洛阳,471003
  2. 机器人及智能系统河南省重点实验室,河南,洛阳,471003
  3. 机械装备先进制造河南省协同创新中心,河南,洛阳,471003
  4. 河南科技大学第一附属医院,河南,洛阳,471003
  5. 郑州轻工业大学机电工程学院,郑州,450002
- 作者简介：
  
  : 2023-01-03。作者简介: 李辽远(1989—),男,博士生
  韩建海(通信作者),男,教授,博士生导师。基金项目: 河南省科技攻关资助项目(212102310890,212102310249)。
- 基金信息：
  
  : 2023-01-03。作者简介: 李辽远(1989—),男,博士生;韩建海(通信作者),男,教授,博士生导师。基金项目: 河南省科技攻关资助项目(212102310890,212102310249)。
- DOI：10.7652/xjtuxb202307002
  中图分类号： TP242
- 网络首发：2023-07-10，
  
  纸质出版：2023
- 稿件说明：
移动端阅览
李辽远, 韩建海, 李向攀, 等. 速度场与意图角双重变导纳主动康复训练策略研究[J]. 西安交通大学学报, 2023,57(7):9-19.

LI Liaoyuan, HAN Jianhai, LI Xiangpan, et al. Research on Active Rehabilitation Training Strategy Based on Dual Variable Admittance of Velocity Field and Intention Angle[J]. 2023, 57(7): 9-19.
李辽远, 韩建海, 李向攀, 等. 速度场与意图角双重变导纳主动康复训练策略研究[J]. 西安交通大学学报, 2023,57(7):9-19. DOI： 10.7652/xjtuxb202307002.

LI Liaoyuan, HAN Jianhai, LI Xiangpan, et al. Research on Active Rehabilitation Training Strategy Based on Dual Variable Admittance of Velocity Field and Intention Angle[J]. 2023, 57(7): 9-19. DOI： 10.7652/xjtuxb202307002.

摘要

针对主动康复训练中出现训练懈怠、控制器和训练表现表征指标复杂、训练任务不能动态调整的问题

提出基于速度场与意图角的双重变导纳控制策略。在期望轨迹周围设计速度场后基于双曲正切函数将患者主动力映射到导纳参数中

设计变导纳控制器。只有患者施加主动力时

导纳控制才能输出机器人运动的驱动力

迫使患者主动参与。将末端交互力矢量与速度矢量的夹角定义为意图角

利用位于[0

π/2)内意图角出现的时间在单次训练时间中的占比γ表征患者的主动控制能力。训练中

根据上一轮的γ来修改导纳参数的上限值

从而改变训练任务的难易程度。在控制策略下进行了6轮训练

结果表明:提出的主动训练策略能够在患者施加主动力的情况下实现三维轨迹允许误差范围内的无时序跟踪训练; 根据设计的表征指标

导纳参数可以从1 100变为1 200再变到1 100

单轮的训练时间从45.6 s变为31.82 s再变回46.9 s

期间患者输出的主动力总和从892.2 N变为694.4 N再变到1 014 N。训练策略可以根据表征指标来判断患者的主动训练能力并调整训练任务

可用于主动康复训练。

Abstract

This paper proposes a dual variable admittance control strategy based on velocity field and intention angle to address the problems of training slackness

complex controller and training performance indicators

and non-adjustable training task in active rehabilitation training. After a velocity field is constructed around the desired trajectory

the variable admittance controller is designed by mapping the patient's active force to the admittance parameter based on the hyperbolic tangent function. Only when the patient exerts the active force

the admittance control outputs the driving force of the robot movement

forcing the patient to participate actively. The angle between the interaction force vector and velocity vector is defined as the “intention angle”. The proportion γ of the time when the intention angle within [0

π/2)appears in a single training cycle is used to represent the patient's active control ability. The upper bound of the admittance parameter will be modified by γ of the previous cycle to adjust the intension of the training task. Six training cycles were performed under the control strategy and the experimental results show that the proposed active training strategy allows tracked training along three-dimensional trajectory without timing sequence within an allowable error range on the condition that the patient participates actively. According to the indicator γ

the admittance parameter may change from 1 100 to 1 200 and back to 1 100

the training duration of a single cycle may change from 45.6 s to 31.82 s and back to 46.9 s

during which the total active force output of the patient may change from 892.2 N to 694.4 N and back to 1 014 N. It indicates that the training strategy determines the patient's active training ability and adjusts the training task accordingly; therefore it can be adopted for active rehabilitation training.

关键词

Keywords

references

GASSERT R, DIETZ V. Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective [J]. Journal of NeuroEngineering and Rehabilitation, 2018, 15(1): 46.

WARRAICH Z, KLEIM J A. Neural plasticity: the biological substrate for neurorehabilitation [J].PM R:The Journal of Injury, Function, and Rehabilitation, 2010, 2(12S): S208-S219.

WOLBRECHT E T, CHAN V, REINKENSMEYER D J, et al. Optimizing compliant, model-based robotic assistance to promote neurorehabilitation [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2008, 16(3): 286-297.

LUO Lincong, PENG Liang, WANG Chen, et al. An assist-as-needed controller for robotic rehabilitation therapy based on RBF network [C]//2018 International Joint Conference on Neural Networks(IJCNN). Piscataway, NJ, USA: IEEE, 2018: 1-7.

苗青, 孙晨阳, 张明明, 等. 基于任务表现的机器人辅助康复自适应控制策略 [J]. 机器人, 2021, 43(5): 539-546, 556.

MIAO Qing, SUN Chenyang, ZHANG Mingming, et al. Performance-based adaptive control strategy for robot-assisted rehabilitation [J]. Robot, 2021, 43(5): 539-546, 556.

WANG Jiajin, ZHANG Jiaji, ZUO Guokun, et al. A reward-punishment feedback control strategy based on energy information for wrist rehabilitation [J]. International Journal of Advanced Robotic Systems, 2020, 17(5): 1729881420940651.

陈盛, 严以哲, 徐国政, 等. 面向肩关节自适应调节助力的镜像式主动康复训练 [J]. 生物医学工程学杂志, 2021, 38(2): 351-360.

CHEN Sheng, YAN Yizhe, XU Guozheng, et al. Mirror-type rehabilitation training with dynamic adjustment and assistance for shoulder joint [J]. Journal of Biomedical Engineering, 2021, 38(2): 351-360.

PEHLIVAN A U, LOSEY D P, O'MALLEY M K. Minimal assist-as-needed controller for upper limb robotic rehabilitation [J]. IEEE Transactions on Robotics, 2016, 32(1): 113-124.

ZHANG Xiaodong, YIN Gui, LI Hanzhe, et al. An adaptive seamless assist-as-needed control scheme for lower extremity rehabilitation robots [J]. Proceedings of the Institution of Mechanical Engineers: Part I Journal of Systems and Control Engineering, 2021, 235(6): 723-734.

梁旭, 王卫群, 苏婷婷, 等. 下肢康复机器人的主动柔顺自适应交互控制 [J]. 机器人, 2021, 43(5): 547-556.

LIANG Xu, WANG Weiqun, SU Tingting, et al. Active compliant and adaptive interaction control for a lower limb rehabilitation robot [J]. Robot, 2021, 43(5): 547-556.

ERDOGAN A, PATOGLU V. Online generation of velocity fields for passive contour following [C]//2011 IEEE World Haptics Conference. Piscataway, NJ, USA: IEEE, 2011: 245-250.

SHAHBAZI M, ATASHZAR S F, TAVAKOLI M, et al. Robotics-assisted mirror rehabilitation therapy: a therapist-in-the-loop assist-as-needed architecture [J]. IEEE/ASME Transactions on Mechatronics, 2016, 21(4): 1954-1965.

LI Yanfang, PATOGLU V, O'MALLEY M K. Negative efficacy of fixed gain error reducing shared control for training in virtual environments [J]. ACM Transactions on Applied Perception, 2009, 6(1): 3.

ZHANG Leigang, GUO Shuai, SUN Qing. An assist-as-needed controller for passive, assistant, active, and resistive robot-aided rehabilitation training of the upper extremity [J]. Applied Sciences, 2021, 11(1): 340.

徐国政, 宋爱国, 高翔, 等. 基于情绪感知的机器人辅助主动康复训练任务控制方法 [J]. 机器人, 2018, 40(4): 466-473.

XU Guozheng, SONG Aiguo, GAO Xiang, et al. Control method for robot-aided active rehabilitation training tasks based on emotion perception [J]. Robot, 2018, 40(4): 466-473.

CHIA E Y, CHEN Yilian, CHIEN T C, et al. Velocity field based active-assistive control for upper limb rehabilitation exoskeleton robot [C]//2020 IEEE International Conference on Robotics and Automation(ICRA). Piscataway, NJ, USA: IEEE, 2020: 1742-1748.

方晓柯, 韩冰, 朱雪枫, 等. 基于速度场的上肢康复机器人的主动控制策略 [J]. 东北大学学报(自然科学版), 2018, 39(2): 153-157, 171.

FANG Xiaoke, HAN Bing, ZHU Xuefeng, et al. Active control strategy for upper limb rehabilitation robot based on velocity field [J]. Journal of Northeastern University(Natural Science), 2018, 39(2): 153-157, 171.

ASL H J, YAMASHITA M, NARIKIYO T, et al. Field-Based assist-as-needed control schemes for rehabilitation robots [J]. IEEE/ASME Transactions on Mechatronics, 2020, 25(4): 2100-2111.

ASL H J, NARIKIYO T. An assistive control strategy for rehabilitation robots using velocity field and force field [C]//2019 IEEE 16th International Conference on Rehabilitation Robotics(ICORR). Piscataway, NJ, USA: IEEE, 2019: 790-795.

ERDOGAN A, PATOGLU V. Slacking prevention during assistive contour following tasks with guaranteed coupled stability [C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2012: 1587-1594.

LI P Y, HOROWITZ R. Passive velocity field control of mechanical manipulators [J]. IEEE Transactions on Robotics and Automation, 1999, 15(4): 751-763.

CAO Jinghui, XIE Shengquan, DAS R, et al. Control strategies for effective robot assisted gait rehabilitation: the state of art and future prospects [J]. Medical Engineering Physics, 2014, 36(12): 1555-1566.

SAMUEL O W, ASOGBON M G, GENG Yanjuan, et al. Decoding movement intent patterns based on spatiotemporal and adaptive filtering method towards active motor training in stroke rehabilitation systems [J]. Neural Computing and Applications, 2021, 33(10): 4793-4806.

GUIDALI M, KELLER U, KLAMROTH-MARGANSKA V, et al. Estimating the patient's contribution during robot-assisted therapy [J]. Journal of Rehabilitation Research and Development, 2013, 50(3): 379-394.

YANG Ziyi, GUO Shuxiang, LIU Yi, et al. An intention-based online bilateral training system for upper limb motor rehabilitation [J]. Microsystem Technologies, 2021, 27(1): 211-222.

MENG Qiaoling, YUE Yiming, LI Sujiao, et al. Electromyogram-based motion compensation control for the upper limb rehabilitation robot in active training [J]. Mechanical Sciences, 2022, 13(2): 675-685.

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