临床研究

实时视觉轨迹反馈对上肢运动学习的影响

  • 李娜 ,
  • 崔志琴 ,
  • 施长城 ,
  • 左国坤
展开
  • 1. 中北大学,山西太原市 030051
    2. 中国科学院宁波材料技术与工程研究所,慈溪生物医学工程研究所,浙江宁波市 315399
    3. 中国科学院宁波材料技术与工程研究所,先进制造技术研究所,浙江宁波市 315201
李娜(1994-),女,汉族,山西晋中市人,硕士研究生,主要研究方向:康复工程。

收稿日期: 2018-12-05

  修回日期: 2019-01-29

  网络出版日期: 2019-04-01

基金资助

1.国家自然科学基金项目(No. 11504372);2.宁波市社会发展重大科技专项(No. 2016C11021;

Effect of Real-time Visual Trajectory Feedback on Upper Limb Movement Learning

  • LI Na ,
  • CUI Zhi-qin ,
  • SHI Chang-cheng ,
  • ZUO Guo-kun
Expand
  • 1. North University of China, Taiyuan, Shanxi 030051, China
    2. Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315399, China
    3. Institute of Advanced Manufacturing Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, China

Received date: 2018-12-05

  Revised date: 2019-01-29

  Online published: 2019-04-01

Supported by

National Natural Science Foundation of China (No. 11504372), Major Scientific and Technological Projects for Social Development in Ningbo City (No. 2016C11021; No. 20181ZDYF020153) and Key Research and Development Program of Zhejiang (No. 2019C03090)

摘要

目的 探究健康志愿者使用康复机器人进行上肢画圆运动中实时视觉轨迹反馈对于志愿者运动学习能力的影响。方法 2018年7至9月,右利手健康志愿者24例按运动能力基准水平测试分为两组,一组包含视觉、成绩和实时轨迹三种反馈(V+KP+KR组),另一组仅包含视觉和成绩两种反馈(V+KR组),两组均进行各自不同条件下的顺时针画圆任务训练,每节20次,共5节,3 d后进行1节保持测试。通过设计的圆度偏移量(RS)和完成时间两个参数对两组运动过程进行评估。结果 两因素重复测量方差分析显示,RS值组间和组内均存在交互作用(F = 6.029, P < 0.001);分析组间单独效应,除第5节训练外(F = 8.775, P = 0.658),V+KP+KR组RS值均显著高于V+KR组(F > 13.126, P < 0.001);分析组内单独效应,两组组内都有非常高度显著性差异(F > 12.465, P < 0.001)。对于完成时间,组间和组内不存在交互作用(F = 1.606, P = 0.228),组间主效应不显著(F = 0.55, P = 0.477),组内主效应显著(F = 3.95, P = 0.031)。结论 实时轨迹反馈可以帮助志愿者在运动学习早期阶段快速掌握运动任务要领,从而帮助其提升新任务的快速学习能力,但在运动学习中后期对于运动学习能力的提升效果不显著。志愿者在运动学习中后期对于实时轨迹反馈的依赖性逐渐减少,进而更加依靠运动本体感觉来完成任务。

本文引用格式

李娜 , 崔志琴 , 施长城 , 左国坤 . 实时视觉轨迹反馈对上肢运动学习的影响[J]. 中国康复理论与实践, 2019 , 25(2) : 217 -223 . DOI: 10.3969/j.issn.1006-9771.2019.02.017

Abstract

Objective To explore the effect of real-time visual trajectory feedback on the motor learning ability of healthy volunteers during upper limb circle-drawing movements by using rehabilitation robots. Methods From July to September, 2018, 24 right-handed healthy volunteers were divided into two groups according to the baseline level of motor ability, one group received three kinds of feedbacks including visual, result and real-time trajectory feedbacks (V+KP+KR group), and the other group only received two kinds of feedbacks including visual and performance (V+KR group). Both groups were trained in clockwise circle drawing task under different conditions for five sessions, 20 times per session, and then underwent one session of maintenance test after three days. The motor learning processes of two groups were evaluated and analyzed by two parameters including roundness score and completion time. Results By using two-factor repeated measures variance analysis, it was found that there were intergroup and intragroup interactions (F = 6.029, P < 0.001). The intergroup individual effects showed that except for the fifth training section (F = 8.775, P = 0.658), the values of roundness score in other sessions were higher in V+KP+KR group than in V+KR group (F > 13.126, P < 0.001). The intragroup individual effects showed that there were significant differences among sessions in both groups (F > 12.465, P < 0.001). For completion time, there was no intergroup and intragroup interactions (F = 1.606, P = 0.228), the intergroup main effect was not significant (F = 0.55, P = 0.477), and the intragroup main effect was significant (F = 3.95, P = 0.031). Conclusion Real-time trajectory feedback can help volunteers quickly understand the essentials of motor tasks at the early stage of motor learning, thus helping them to improve the fast learning ability of new tasks, but the improvement effect on motor learning during middle and late stages is not obvious. The dependence of volunteers on real-time trajectory feedback gradually decreases in the middle and later stages of motor learning, and they might rely on proprioception to complete tasks.

参考文献

[1] 王陇德,王金环,彭斌,等. 《中国脑卒中防治报告2016》概要 [J]. 中国脑血管病杂志, 2017, 14(4): 217-224.
[2] 王陇德. 中国脑卒中防治工程的现状与未来[J]. 第二军医大学学报, 2018, 39(9): 941-943.
[3] 李周,周虹,孙莹,等. 综合康复评估及训练系统在脑卒中患者上肢及手的运动控制训练中的应用[J]. 中国康复医学杂志, 2013, 28(7): 663-664.
[4] 王丽,张秀峰,马岩,等. 脑卒中患者上肢康复机器人及评价方法综述[J]. 北京生物医学工程, 2015, 34(5): 526-532.
[5] Krebs H I, Aisen M L, Volpe B T, et al. Quantization of continuous arm movements in humans with brain injury [J]. Proc Natl Acad Sci U S A, 1999, 96(8): 4645-4649.
[6] 邵丽,赵永波. 脑卒中康复与神经可塑性 [J]. 中风与神经疾病杂志, 2006, 23(1): 126-128.
[7] Krebs H I, Palazzolo J J, Dipietro L, et al. Rehabilitation robotics: performance-based progressive robot-assisted therapy [J]. Auton Robot, 2003, 15(1): 7-20.
[8] Marchal-Crespo L, Reinkensmeyer D J. Review of control strategies for robotic movement training after neurologic injury [J]. J Neuroeng Rehabil, 2009, 6: 20.
[9] Lotze M, Braun C, Birbaumer N, et al. Motor learning elicited by voluntary drive [J]. Brain, 2003, 126(4): 866-872.
[10] Krakauer J W. The applicability of motor learning to neurorehabilitation [M]//Dietz V, Ward N. Oxford Textbook of Neurorehabilitation. Oxford, UK: OUP Oxford, 2015: 55-63.
[11] Crum E O, Baltz M J, Krause D A. The use of motor learning and neural plasticity in rehabilitation for ataxic hemiparesis: a case report [J]. Phys Theory Pract, 2019: 1-10. doi: 10.1080/09593985.2019.1566941.
[Epub ahead of print].
[12] Sigrist R, Rauter G, Riener R, et al. Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review [J]. Psychon Bull Rev, 2013, 20(1): 21-53.
[13] Bernier P M, Chua R, Franks I M. Is proprioception calibrated during visually guided movements? [J]. Exp Brain Res, 2005, 167(2): 292-296.
[14] Robin C, Toussaint L, Blandin Y, et al. Specificity of learning in a video-aiming task: modifying the salience of dynamic visual cues [J]. J Mot Behav, 2005, 37(5): 367-376.
[15] Hinder M R, Tresilian J R, Riek S, et al. The contribution of visual feedback to visuomotor adaptation: how much and when? [J]. Brain Res, 2008, 1197: 123-134.
[16] Blandin Y, Toussaint L, Shea C H. Specificity of practice: interaction between concurrent sensory information and terminal feedback [J]. J Exp Psychol Learn Mem Cogn, 2008, 34(4): 994-1000.
[17] Toussaint L, Meugnot A, Badets A, et al. The specificity of practice hypothesis in goal-directed movements: visual dominance or proprioception neglect? [J]. Psychol Res, 2017, 81(2): 407-414.
[18] Goodman R, Crainic V A, Bested S R, et al. Amending ongoing upper-limb reaches: visual and proprioceptive contributions? [J]. Multisens Res, 2018, 31(5): 455-480.
[19] Sharer E A, Mostofsky S H, Pascual-Leone A, et al. Isolating visual and proprioceptive components of motor sequence learning in ASD [J]. Autism Res, 2016, 9(5): 563-569.
[20] Gaffin-Cahn E, Hudson T E, Landy M S. Did I do that? Detecting a perturbation to visual feedback in a reaching task [J]. J Vis, 2019, 19(1): 5, 1-18.
[21] Sugiyama T, Liew S L. The Effects of sensory manipulations on motor behavior: from basic science to clinical rehabilitation [J]. J Mot Behav, 2017, 49(1): 67-77.
[22] Tremblay L, Proteau L. Specificity of practice: the case of powerlifting [J]. Res Q Exerc Sport, 1998, 69(3): 284-289.
[23] Marchal-Crespo L, Rappo N, Riener R. The effectiveness of robotic training depends on motor task characteristics [J]. Exp Brain Res, 2017, 235(12): 3799-3816.
[24] 金亚虹,章建成,任杰. 视觉反馈对结果反馈时间点适宜值的影响 [J]. 北京体育大学学报, 2010, 33(12): 68-71.
[25] Kuling I A, de Brouwer A J, Smeets J B J, et al. Correcting for natural visuo-proprioceptive matching errors based on reward as opposed to error feedback does not lead to higher retention [J]. Exp Brain Res, 2018. doi: 10.1007/s00221-018-5456-3.
[Epub ahead of print]
[26] Wei Y, Bajaj P, Scheidt R, et al. Visual error augmentation for enhancing motor learning and rehabilitative relearning [C]. Institute of Electrical and Electronics Engineers, 2005.
[27] Fleishman E A, Rich S. Role of kinesthetic and spatialvisual abilities in perceptual-motor learning [J]. J Exp Psychol, 1963, 66: 6-11.
[28] Shadmehr R, Krakauer J W. A computational neuroanatomy for motor control [J]. Exp Brain Res, 2008, 185(3): 359-381.
[29] Shadmehr R, Mussaivaldi S. Biological Learning and Control: How the Brain Builds Representations, Predicts Events, and Makes Decisions [M]. Cambridge, MA, USA: MIT Press, 2012.
[30] van der Kooij K, Overvliet K E, Smeets J B J. Temporally stable adaptation is robust, incomplete and specific [J]. Eur J Neurosci, 2016, 44(9): 2708-2715.
[31] Crevecoeur F, Munoz D P, Scott S H. Dynamic multisensory integration: somatosensory speed trumps visual accuracy during feedback control [J]. J Neurosci, 2016, 36(33): 8598-8611.
[32] Barros J A C, Yantha Z D, Carter M J, et al. Examining the impact of error estimation on the effects of self-controlled feedback [J]. Hum Mov Sci, 2019, 63: 182-198.
[33] Wei K, Kording K. Relevance of error: what drives motor adaptation? [J]. J Neurophysiol, 2009, 101(2): 655-664.
[34] Reinkensmeyer D J, Housman S J. "If I can't do it once, why do it a hundred times?": Connecting volition to movement success in a virtual environment motivates people to exercise the arm after stroke [C]. Virtual Rehabilitation, IEEE, 2007.
[35] Guadagnoli M A, Lee T D. Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning [J]. J Mot Behav, 2004, 36(2): 212-224.
[36] Wulf G, Lewthwaite R. Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning [J]. Psychon Bull Rev, 2016, 23(5): 1382-1414.
[37] Lewthwaite R, Wulf G. Optimizing motivation and attention for motor performance and learning [J]. Curr Opin Psychol, 2017, 16: 38-42.
[38] Salmoni A W, Schmidt R A, Walter C B. Knowledge of results and motor learning: a review and critical reappraisal [J]. Psychol Bull, 1984, 95(3): 355-386.
[39] Pila O, Duret C, Laborne F X, et al. Pattern of improvement in upper limb pointing task kinematics after a 3-month training program with robotic assistance in stroke [J]. J Neuroeng Rehabil, 2017, 14(1): 105.
文章导航

/

〈 〉