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| Gait matching method of rehabilitation training for exoskeleton considering embodied cognition |
Yicong GAO1( ),Yuejin WANG1,Yilei CHEN2,Hao ZHENG3,*(),Jianrong TAN1 |
1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
2. Sir Run Run Shaw Hospital, Medical College of Zhejiang University, Hangzhou 310016, China
3. Hangzhou Innovation Institute of Beihang University, Hangzhou 310056, China |
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Abstract A lower limb exoskeleton gait matching method based on embodied cognition was proposed in order to address the issue of personalized gait adaptation in rehabilitation training. Bodily experiences and sensory feedback were integrated into gait evaluation, and an evaluation index for gait adaptation that considered embodied cognition was established. Fuzzy language evaluation based on Z-number was used to assess the gaits, and evidence reasoning under fuzzy uncertainty was applied to integrate confidence in the evaluations. Then traditional methods that struggle with the interdependence of natural language in gait evaluation were improved, allowing for rapid iterative matching of suitable gaits during rehabilitation. The validity of the method was verified through experiments.
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Received: 11 July 2024
Published: 28 July 2025
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| Fund: 国家自然科学基金资助项目(52375272)；中国博士后科学基金资助项目（2024M751644, 2024M754069）；国家资助博士后研究人员计划资助项目（GZC20241092）；浙江省重点研发计划资助项目（2021C03050）. |
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Corresponding Authors:
Hao ZHENG
E-mail: gaoyicong@zju.edu.cn;haozheng@zjut.edu.cn
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考虑具身认知的外骨骼康复训练步态匹配方法
针对康复训练步态的个性化适配问题，提出考虑具身认知的下肢外骨骼康复训练步态匹配方法. 将身体体验和感官感受作为反馈引入下肢外骨骼步态评价中，构建考虑具身认知的康复训练步态适配评价指标. 利用基于Z-number的模糊语言术语进行康复训练步态的模糊语言评价，通过模糊不确定性下的证据推理方法融合康复训练步态评价置信度，克服了传统方法无法处理非相互独立的康复训练步态评价自然语言术语的局限性，在康复训练过程中快速迭代匹配合适的康复训练步态. 通过实验，验证本文所提方法的正确性.
关键词：
下肢外骨骼,
具身认知,
康复训练步态,
Z-number
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|
| [1] |
GBD 2016 Causes of Death Collaborators Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the global burden of disease study 2016[J]. Lancet, 2017, 390 (10100): 1151- 1210
doi: 10.1016/S0140-6736(17)32152-9
|
|
|
| [2] |
王拥军, 李子孝, 谷鸿秋, 等 中国卒中报告2020(中文版)(1)[J]. 中国卒中杂志, 2022, 17 (5): 433- 447
WANG Yongjun, LI Zixiao, GU Hongqiu, et al China Stroke Report 2020 (Chinese version)(1)[J]. Chinese Journal of Stroke, 2022, 17 (5): 433- 447
|
|
|
| [3] |
DUNCAN M J, VAN W F, POLLOCK A, et al Outcome measures in post-stroke arm rehabilitation trails: do existing, measures capture outcomes that are important to stroke survivors, carers, and clinicians?[J]. Clinical Rehabilitation, 2019, 33 (4): 737- 749
doi: 10.1177/0269215518823248
|
|
|
| [4] |
陈晓艳, 王娅, 黄娟, 等 脑卒中患者早期肌力训练的最佳证据总结[J]. 中华护理杂志, 2020, 55 (8): 1253- 1259
CHEN Xiaoyan, WANG Ya, HUANG Juan, et al Summary of the best evidence of early muscle strength training in stroke patients[J]. Chinese Journal of Nursing, 2020, 55 (8): 1253- 1259
|
|
|
| [5] |
HORSTMAN A M, BELTMAN M J, GERRITS K H, et al Intrinsic muscle strength and voluntary activation of both lower limbs and functional performance after stroke[J]. Clinical Physiology and Functional Imaging, 2008, 28 (4): 251- 261
doi: 10.1111/j.1475-097X.2008.00802.x
|
|
|
| [6] |
BERGMANN J, KREWER C, JAHN K, et al Robot-assisted gait training to reduce pusher behavior: a randomized controlled trail[J]. Neurology, 2018, 91 (14): e1319- e1327
|
|
|
| [7] |
程雪. 下肢康复机器人对慢性期脑卒中辅助步态训练康复效果的初步研究[D]. 重庆: 重庆医科大学, 2020.
CHENG Xue. Preliminary study on rehabilitation effects of the A3 robot-assisted gait training on patients with chronic stroke [D]. Chongqing: Chongqing Medical University, 2020.
|
|
|
| [8] |
高一聪, 王彦坤, 郑浩, 等 下肢康复外骨骼的步态交互设计研究进展[J]. 机械工程学报, 2023, 59 (17): 175- 188
GAO Yicong, WANG Yankun, ZHENG Hao, et al Gait interaction design of lower limb rehabilitation exoskeletons: a review[J]. Journal of Mechanical Engineering, 2023, 59 (17): 175- 188
doi: 10.3901/JME.2023.17.175
|
|
|
| [9] |
LEE D, KANG I, MOLINARO D D, et al Real-time user-independent slope prediction using deep learning for modulation of robotic knee exoskeleton assistance[J]. IEEE Robotics and Automation Letters, 2021, 6 (2): 3995- 4000
doi: 10.1109/LRA.2021.3066973
|
|
|
| [10] |
李雨戈. 基于人肢体间运动协同规律的髋关节轨迹生成方法研究[D]. 武汉: 华中科技大学, 2023.
LI Yuge. Research on hip joint trajectory generation method based on human limb motion synergy law [D]. Wuhan: Huazhong University of Science and Technology, 2023.
|
|
|
| [11] |
SHIXIN R, WEIQUN W, ZENGGUANG H, et al Personalized gait trajectory generation based on anthropometric features using random forest[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 14 (1): 15597- 15608
|
|
|
| [12] |
SHARMA R, GAUR P, BHATT S, et al Optimal fuzzy logic-based control strategy for lower limb rehabilitation exoskeleton[J]. Applied Soft Computing, 2021, 105 (1): 107226
|
|
|
| [13] |
王晓峰, 梁亮 一种下肢康复机器人步态的生成与调节方法[J]. 控制工程, 2022, 29 (1): 18- 26
WANG Xiaofeng, LIANG Liang A method of gait pattern generating and regulating for lower limb rehabilitation exoskeleton robot[J]. Control Engineering of China, 2022, 29 (1): 18- 26
|
|
|
| [14] |
SHARIFI M, MEHR J K, MUSHAHWAR V K, et al Adaptive CPG-based gait planning with learning-based torque estimation and control for exoskeletons[J]. IEEE Robotics and Automation Letters, 2021, 6 (4): 8261- 8268
doi: 10.1109/LRA.2021.3105996
|
|
|
| [15] |
HWANG S H, SUN D I, HAN J, et al Gait pattern generation algorithm for lower-extremity rehabilitation–exoskeleton robot considering wearer’s condition[J]. Intelligent Service Robotics, 2021, 14 (3): 345- 355
doi: 10.1007/s11370-020-00346-3
|
|
|
| [16] |
QIN T, YANG Y, WEN B, et al Research on human gait prediction and recognition algorithm of lower limb-assisted exoskeleton robot[J]. Intelligent Service Robotics, 2021, 14 (3): 445- 457
doi: 10.1007/s11370-021-00367-6
|
|
|
| [17] |
程洪, 黄瑞, 邱静, 等 康复机器人及其临床应用综述[J]. 机器人, 2021, 43 (5): 606- 619
CEHNG Hong, HUANG Rui, QIU Jing, et al A survey of rehabilitation robot and its clinical applications[J]. Robot, 2021, 43 (5): 606- 619
|
|
|
| [18] |
WU X, LIU D X, LIU M, et al Individualized gait pattern generation for sharing lower limb exoskeleton robot[J]. IEEE Transactions on Automation Science and Engineering, 2018, 15 (4): 1459- 1470
doi: 10.1109/TASE.2018.2841358
|
|
|
| [19] |
TUCKER M, NOVOSELLER E, KANN C, et al. Preference-based learning for exoskeleton gait optimization [C]//IEEE International Conference on Robotics and Automation. Paris: IEEE, 2020: 2351-2357.
|
|
|
| [20] |
陆康. 面向偏瘫康复的下肢外骨骼机器人步态预测[D]. 贵阳: 贵州大学, 2023.
LU Kang. Gait prediction of lower limb exoskeleton robot for hemiplegia rehabilitation [D]. Guiyang: Guizhou University, 2023.
|
|
|
| [21] |
程鑫. 下肢外骨骼机器人的步态评估方法及应用研究[D]. 成都: 电子科技大学, 2020.
CHENG Xin. Research on the method and application of gait assessment of lower limb exoskeleton robot [D]. Chengdu: University of Electronic Science and Technology, 2020.
|
|
|
| [22] |
李浩范 Rivermead运动指数的简要回顾[J]. 国外医学(物理医学与康复学分册), 2000, 1 (4): 189
LI Haofan Brief review of Rivermead motion index[J]. Foreign Medicine (Physical Medicine and Rehabilitation), 2000, 1 (4): 189
|
|
|
| [23] |
王晓艳. 虚拟现实行走智能跑台训练对脑卒中患者平衡及步行能力的影响[D]. 沈阳: 沈阳体育学院, 2021.
WANG Xiaoyan. Effect of virtual reality walking intelligent treadmill training on balance and walking ability of stroke patients [D]. Shenyang: Shenyang Sport University, 2021.
|
|
|
| [24] |
SENDEN R, SAVELBERG H, GRIMM B, et al Accelerometry-based gait analysis, an additional objective approach to screen subjects at risk for falling[J]. Gait and Posture, 2012, 36 (2): 296- 300
doi: 10.1016/j.gaitpost.2012.03.015
|
|
|
| [25] |
GRONLEY J, PERRY J Gait analysis techniques: Rancho Los Amigos hospital gait laboratory[J]. Physical Therapy, 1984, 64 (12): 1831- 1838
doi: 10.1093/ptj/64.12.1831
|
|
|
| [26] |
胡楠, 卢茜, 李军, 等 两种步态量表在脑卒中偏瘫步行评定中的评测者间信度[J]. 中国康复理论与实践, 2015, 21 (5): 549- 551
HU Nan, LU Xi, LI Jun, et al Interrater reliability of two gait scales in walking assessment of stroke hemiplegia[J]. Chinese Journal of Rehabilitation Theory and Practice, 2015, 21 (5): 549- 551
doi: 10.3969/j.issn.1006-9771.2015.05.012
|
|
|
| [27] |
卢利萍, 桑德春, 季淑凤 下肢康复机器人训练对脑卒中偏瘫患者运动能力和日常生活活动能力的影响[J]. 中国康复理论与实践, 2016, 22 (10): 1200- 1203
LU Liping, SANG Dechun, JI Shufeng Effect of leg rehabilitation robot training on motor and activities of daily living in hemiplegic patients after stroke[J]. Chinese Journal of Rehabilitation Theory and Practice, 2016, 22 (10): 1200- 1203
|
|
|
| [28] |
何灿群, 吕晨晨 具身认知视角下的无意识设计[J]. 包装工程, 2020, 41 (8): 80- 86
HE Canqun, LV Chenchen Unconscious design from the perspective of embodied cognition[J]. Packaging Engineering, 2020, 41 (8): 80- 86
|
|
|
| [29] |
刘佳蕾, 刘子建 具身认知视角下膝关节康复机的情感化设计研究[J]. 包装工程, 2019, 40 (22): 203- 207
LIU Jialei, LIU Zijian Emotional design of knee rehabilitation machine from the perspective of embodied cognition[J]. Packaging Engineering, 2019, 40 (22): 203- 207
|
|
|
| [30] |
刘宏宇. 基于具身认知理论的多模态可穿戴产品设计研究[D]. 无锡: 江南大学, 2022.
LIU Hongyu. Research on multimodal wearable products design based on embodied cognition theory [D]. Wuxi: Jiangnan University, 2022.
|
|
|
| [31] |
ZADEH L A Fuzzy sets[J]. Information and Control, 1965, 8 (3): 338- 353
doi: 10.1016/S0019-9958(65)90241-X
|
|
|
| [32] |
糜万俊, 江文奇, 戴跃伟 三角模糊数去模糊化对VIKOR妥协解的影响研究[J]. 运筹与管理, 2019, 28 (10): 77- 82
MI Wanjun, JIANG Wenqi, DAI Yuewei Effect of defuzzifying triangular fuzzy number on compromise solutions in VIKOR[J]. Operations Research and Management Science, 2019, 28 (10): 77- 82
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