Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2022, Vol. 62 Issue (3): 458-462    DOI: 10.16511/j.cnki.qhdxxb.2021.26.027
  机械工程 本期目录 | 过刊浏览 | 高级检索 |
人体下肢运动协调模型
张羽中, 柳世强, 张俊昌, 朱荣
清华大学 精密仪器系, 精密测试技术及仪器国家重点实验室, 北京 100084
Coordination model for human lower limb motion
ZHANG Yuzhong, LIU Shiqiang, ZHANG Junchang, ZHU Rong
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China
全文: PDF(5039 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 人体在行走、奔跑等运动时,在运动神经中枢的支配下,下肢运动存在一定的协调关系。这种协调性是生物进化和后天学习的结果,它使人体运动更加灵活、高效。研究健康人体的下肢运动协调性对于中风病人的康复治疗、足式机器人的步态规划、外骨骼机器人等助力设备的人机协同控制等领域具有重要意义。目前人体下肢协调性的研究仍比较初级,多停留在现象分析和理论表述,缺少准确实用的建模方法。该文利用可穿戴运动捕捉系统对人体下肢在行走和奔跑时的运动协调性进行测量和分析,基于神经网络模型提出大小腿运动协调性的建模方法。实验结果证明了大小腿运动协调模型的存在,基于该模型,可根据小腿的运动信息准确预估大腿和膝关节的运动信息。该研究结果可应用于康复医疗、机器人步态规划、智能助力设备等领域,将积极促进相关领域技术进步。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
张羽中
柳世强
张俊昌
朱荣
关键词 人体运动分析运动协调神经网络可穿戴运动传感器    
Abstract:When a human is walking or running, the coordinated relationship between the movements of the thigh and shank are controlled by the motor nerve system. This relationship between the parts of the lower limb is the result of evolution and experience, which makes human movement more flexible and efficient. Research on the coordination of the lower limbs for healthy people facilitates rehabilitation of stroke patients, robot gait planning, and human-machine collaboration of assistive equipment such as exoskeletons. At present, the research on the coordination of human lower limb motion is still relatively basic, mostly focusing on observations and theoretical analyses, with few practical models. This study used a wearable motion capture system to measure and model the motion relationships of the lower limbs for human walking and running. A neural network model was then developed to characterize the coordination of the thigh and shank. This model accurately predicts the motion of the thigh and the knee joint from the shank movement information. This motion model can be used for rehabilitation, robot gait planning, intelligent assisting equipment and other related fields.
Key wordshuman motion    limb motion coordination    neural networks    wearable motion capture system
收稿日期: 2021-03-03      出版日期: 2022-03-10
基金资助:朱荣,教授,E-mail:zr_gloria@tsinghua.edu.cn
引用本文:   
张羽中, 柳世强, 张俊昌, 朱荣. 人体下肢运动协调模型[J]. 清华大学学报(自然科学版), 2022, 62(3): 458-462.
ZHANG Yuzhong, LIU Shiqiang, ZHANG Junchang, ZHU Rong. Coordination model for human lower limb motion. Journal of Tsinghua University(Science and Technology), 2022, 62(3): 458-462.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2021.26.027  或          http://jst.tsinghuajournals.com/CN/Y2022/V62/I3/458
  
  
  
  
  
  
  
  
[1] KIM J, LEE G, HEIMGARTNER R, et al. Reducing the metabolic rate of walking and running with a versatile, portable exosuit[J]. Science, 2019, 365(6454):668-672.
[2] LACQUANITI F, GRASSO R, ZAGO M. Motor patterns in walking[J]. News in Physiological Sciences, 1999, 14(4):168-174.
[3] BORGHESE N A, BIANCHI L, LACQUANITI F. Kinematic determinants of human locomotion[J]. Journal of Physiology:A Publication of the Physioloical Society, 1996, 494(3):863-879.
[4] BARLIYA A, OMLOR L, GIESE M A, et al. An analytical formulation of the law of intersegmental coordination during human locomotion[J]. Experimental Brain Research, 2009, 193(3):371-385.
[5] APRIGLIANO F, MARTELLI D, TROPEA P, et al. Aging does not affect the intralimb coordination elicited by slip-like perturbation of different intensities[J]. Journal of Neurophysiology, 2017, 118(3):1739-1748.
[6] VILLARREAL D J, POONAWALA H A, GREGG R D. A robust parameterization of human gait patterns across phase-shifting perturbations[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(3):265-278.
[7] FUJIKI S, AOI S, FUNATO T, et al. Adaptation mechanism of interlimb coordination in human split-belt treadmill walking through learning of foot contact timing:A robotics study[J]. Journal of the Royal Society Interface,2015, 12(110):20150542.
[8] AOI S, MANOONPONG P, AMBE Y, et al. Adaptive control strategies for interlimb coordination in legged robots:A review[J]. Frontiers in Neurorobotics, 2017, 11(8):39.
[9] ZAJAC F E, NEPTUNE R R, KAUTZ S A. Biomechanics and muscle coordination of human walking:Part I:Introduction to concepts, power transfer, dynamics and simulations[J]. Gait & Posture, 2002, 16(3):215-232.
[10] SOUSA A S P, TAVARES J M R S. Interlimb coordination during step-to-step transition and gait performance[J]. Journal of Motor Behavior, 2015, 47(6):563-574.
[11] SATO Y, YAMADA N. Interlimb coordination of ground reaction forces during double stance phase at fast walking speed[J]. Advances in Physical Education, 2018, 8(2):263-273.
[12] DING Y, KIM M, KUINDERSMA S, et al. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking[J]. Science Robotics, 2018, 3(15):eaar5438.
[13] ZHANG J C, LIU S Q, ZHANG Y Z, et al. A method to extract motion velocities of limb and trunk in human locomotion[J]. IEEE Access, 2020(8):120553-120561.
[14] LIU S Q, ZHANG J C, ZHU R. A wearable human motion tracking device using micro flow sensor incorporating a micro accelerometer[J]. IEEE Transactions on Biomedical Engineering, 2020, 67(4):940-948.
[15] ZHANG J C, LIU S Q, ZHU R. Motion velocity, acceleration and energy expenditure estimation using micro flow sensor[J]. IEEE Access, 2019(7):75901-75909.
[16] LIU S Q, ZHANG J C, LI G Z, et al. A wearable flow-MIMU device for monitoring human dynamic motion[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(3):637-645.
[17] LIU S Q, ZHANG J C, ZHANG Y Z, et al. A wearable motion capture device able to detect dynamic motion of human limbs[J]. Nature Communications, 2020, 11(1):5615.
[18] QUE R Y, ZHU R. A compact flexible thermal flow sensor for detecting two-dimensional flow vector[J]. IEEE Sensors Journal, 2015, 15(3):1931-1936.
[19] LIU P, ZHU R, QUE R Y. A flexible flow sensor system and its characteristics for fluid mechanics measurements[J]. Sensors, 2009, 9(12):9533-9543.
[20] ZHAO S, JIANG P, ZHU R, et al. Wearable anemometer for 2D wind detection[C]//2016 IEEE Sensors. Orlando, USA:IEEE, 2016:1-3.
[21] LI G Z, ZHAO S, ZHU R. Wearable anemometer with multi-sensing of wind absolute orientation, wind speed, attitude, and heading[J]. IEEE Sensors Journal, 2019, 19(1):297-303.
[22] SEITERLE S, SUSKO T, ARTEMIADIS P K, et al. Interlimb coordination in body-weight supported locomotion:A pilot study[J]. Journal of Biomechanics, 2015, 48(11):2837-2843.
[23] HAMNER S R, DELP S L. Muscle contributions to fore-aft and vertical body mass center accelerations over a range of running speeds[J]. Journal of Biomechanics, 2013, 46(4):780-787.
[24] LIU M Q, ANDERSON F C, SCHWARTZ M H, et al. Muscle contributions to support and progression over a range of walking speeds[J]. Journal of Biomechanics, 2008, 41(15):3243-3252.
[1] 张雪芹, 刘岗, 王智能, 罗飞, 吴建华. 基于多特征融合和深度学习的微观扩散预测[J]. 清华大学学报(自然科学版), 2024, 64(4): 688-699.
[2] 张名芳, 李桂林, 吴初娜, 王力, 佟良昊. 基于轻量型空间特征编码网络的驾驶人注视区域估计算法[J]. 清华大学学报(自然科学版), 2024, 64(1): 44-54.
[3] 杨波, 邱雷, 吴书. 异质图神经网络协同过滤模型[J]. 清华大学学报(自然科学版), 2023, 63(9): 1339-1349.
[4] 付雯, 温浩, 黄俊珲, 孙镔轩, 陈嘉杰, 陈武, 冯跃, 段星光. 基于非线性动力学模型补偿的水下机械臂自适应滑模控制[J]. 清华大学学报(自然科学版), 2023, 63(7): 1068-1077.
[5] 黄贲, 康飞, 唐玉. 基于目标检测的混凝土坝裂缝实时检测方法[J]. 清华大学学报(自然科学版), 2023, 63(7): 1078-1086.
[6] 陈波, 张华, 陈永灿, 李永龙, 熊劲松. 基于特征增强的水工结构裂缝语义分割方法[J]. 清华大学学报(自然科学版), 2023, 63(7): 1135-1143.
[7] 代鑫, 黄弘, 汲欣愉, 王巍. 基于机器学习的城市暴雨内涝时空快速预测模型[J]. 清华大学学报(自然科学版), 2023, 63(6): 865-873.
[8] 李聪健, 高航, 刘奕. 基于数值模拟和机器学习的风场快速重构方法[J]. 清华大学学报(自然科学版), 2023, 63(6): 882-887.
[9] 杜晓闯, 梁漫春, 黎岢, 俞彦成, 刘欣, 汪向伟, 王汝栋, 张国杰, 付起. 基于卷积神经网络的γ放射性核素识别方法[J]. 清华大学学报(自然科学版), 2023, 63(6): 980-986.
[10] 安健, 陈宇轩, 苏星宇, 周华, 任祝寅. 机器学习在湍流燃烧及发动机中的应用与展望[J]. 清华大学学报(自然科学版), 2023, 63(4): 462-472.
[11] 孙继昊, 宋颖, 石云姣, 赵宁波, 郑洪涛. 天然气同轴分级燃烧室污染物生成及预测[J]. 清华大学学报(自然科学版), 2023, 63(4): 649-659.
[12] 刘江帆, 葛冰, 李珊珊, 芦翔. 基于神经网络的燃烧室壁面冷效预测方法[J]. 清华大学学报(自然科学版), 2023, 63(4): 681-690.
[13] 郭世圆, 马为之, 卢瑞麟, 刘晋龙, 杨志刚, 王忠静, 张敏. 基于LSTM神经网络的复杂工况下明渠流量预测[J]. 清华大学学报(自然科学版), 2023, 63(12): 1924-1934.
[14] 邓青, 张博, 李宜豪, 周亮, 周正青, 蒋慧灵, 高扬. 基于级联CNN的疏散场景中人群数量估计模型[J]. 清华大学学报(自然科学版), 2023, 63(1): 146-152.
[15] 庄文宇, 张如九, 徐建军, 殷亮, 魏海宁, 刘耀儒. 基于IAGA-BP算法的高拱坝-坝基力学参数反演分析[J]. 清华大学学报(自然科学版), 2022, 62(8): 1302-1313.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn