基于非接触式电容传感的人体运动意图识别

doi:10.3901/JME.2019.11.019

机械工程学报 ›› 2019, Vol. 55 ›› Issue (11): 19-27.doi: 10.3901/JME.2019.11.019

• 特邀专栏：共融机器人 • 上一篇下一篇

基于非接触式电容传感的人体运动意图识别

王启宁^1,2, 郑恩昊³, 许东方¹, 麦金耿¹

1. 北京大学工学院北京 100871;
2. 北京大学工程科学与新兴技术高精尖创新中心北京 100871;
3. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190

收稿日期:2018-03-31 修回日期:2018-11-21 出版日期:2019-06-05 发布日期:2019-06-05
通讯作者: 王启宁(通信作者),男,1981年出生,博士,研究员,博士研究生导师。主要研究方向为智能机器人和康复工程。E-mail:qiningwang@pku.edu.cn
作者简介:郑恩昊,男,1987年出生,博士,副研究员。主要研究方向为神经接口、人机交互。E-mail:enhao.zheng@ia.ac.cn;许东方,男,1990年出生,博士研究生。主要研究方向为人机接口、仿生材料。E-mail:dongfangxu@pku.edu.cn;麦金耿,男,1982年出生,博士。主要研究方向为并行计算、神经接口。E-mail:jingengmai@pku.edu.cn
基金资助:
国家自然科学基金资助项目（91648207，61703400）。

Noncontact Capacitive Sensing Based Human Motion Intent Recognition

WANG Qining^1,2, ZHENG Enhao³, XU Dongfang¹, MAI Jingeng¹

1. College of Engineering, Peking University, Beijing 100871;
2. Beijing Innovation Center for Engineering Science and Advanced Technology(BIC-ESAT), Beijing 100871;
3. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

Received:2018-03-31 Revised:2018-11-21 Online:2019-06-05 Published:2019-06-05

摘要/Abstract

摘要： 人体运动意图的准确可靠识别是人机共融中的关键问题之一。针对现有研究中的局限和不足，提出了全新的非接触式电容传感方法。该方法以金属电极不接触皮肤的方式测量肌肉收缩信号。介绍了电容传感的原理，分析了基于该方法测量肌肉收缩形状变化的机理。分别介绍了非接触式电容传感方法在小腿智能动力假肢控制和上肢运动识别中的应用。针对下肢智能假肢控制，提出了基于非接触式电容传感的运动模态以及模态切换的识别。为了进一步提高系统的可穿戴性，提出了基于柔性可延展液态金属电极的电容传感系统并进行了初步的试验验证；针对上肢运动识别，首先介绍了用于腕关节离散运动模式的识别研究，其次介绍了基于电容传感对连续握力的识别和估计，证实了电容传感这一全新方法在上肢运动识别中的可行性。未来会在穿戴式机器人控制以及协作性机器人模仿学习中对电容传感方法进行更深入的研究。

关键词: 非接触式电容传感, 人体运动意图识别, 上肢运动识别, 下肢智能假肢

Abstract: Accurate and reliable recognition of human motion intent is an important issue in the field of tri-co robotics. A noncontact capacitive sensing approach is proposed which can measure muscle contraction signals by freeing human skin from contacting to metal electrodes. The method is proposed to address the issues in biological-signal-based human motion sensing. We then introduced four case studies of noncontact capacitive sensing. The first study involved locomotion transition recognition on transtibial amputees wearing lower-limb robotic prostheses. The second one is the initial study on new capacitive sensing electrodes with liquid metal electrode, the purpose of which is to build a soft elastic capacitive sensing front-end for more compatible dressing. By comparison the last two studies are applications of the capacitive sensing method on human upper limb motion recognition to prove the feasibility of the new method on upper limb motion sensing. The third one is the forearm discrete motion pattern recognition and the last case is the continuous grasp force estimation with the capacitance signals. The results of the studies suggest the promise of the new sensing method. In future works, the noncontact capacitive sensing approach will be further studied for the control of wearable robots and collaborative robots.

Key words: human motion recognition, human upper-limb motion sensing, lower-limb intelligent prosthesis, noncontact capacitive sensing

中图分类号:

TG156

王启宁, 郑恩昊, 许东方, 麦金耿. 基于非接触式电容传感的人体运动意图识别[J]. 机械工程学报, 2019, 55(11): 19-27.

WANG Qining, ZHENG Enhao, XU Dongfang, MAI Jingeng. Noncontact Capacitive Sensing Based Human Motion Intent Recognition[J]. Journal of Mechanical Engineering, 2019, 55(11): 19-27.

参考文献

[1] DING H,YANG X,ZHENG N,et al. Tri-co robot:A Chinese robotic research initiative for enhanced robot interaction capabilities[J]. National Science Review,2017,0:1-3.
[2] PONS J L. Wearable robots:Biomechatronic exoskeletons[M]. John Wiley & Sons,2008.
[3] TANG G. The development of a human-robot interface for industrial collaborative system[D]. Cranfield University,2016.
[4] MAGNENAT-THALMANN N,YUAN J,THALMANN D,et al. Context aware human-robot and human-agent interaction[M]. Springer,2016.
[5] GIACOMOZZI C. Appropriateness of plantar pressure measurement devices:A comparative technical assessment[J]. Gait & Posture,2010,32(1):141-144.
[6] HATSOPOULOS N G,DONOGHUE J P. The science of neural interface systems[J]. Annual Review of Neuroscience,2009,32:249-266.
[7] ROETENBERG D,LUINGE H,SLYCKE P. Xsens MVN:Full 6DOF human motion tracking using miniature inertial sensors[J]. Xsens Motion Technologies BV,Tech. Rep,2009(1):1-9.
[8] FONG D T P,CHAN Y Y,HONG Y,et al. Estimating the complete ground reaction forces with pressure insoles in walking[J]. Journal of Biomechanics,2008,41(11):2597-2601.
[9] NOVAK D,RIENER R. A survey of sensor fusion methods in wearable robotics[J]. Robotics and Autonomous Systems,2015,73:155-170.
[10] ENGEL A K,MOLL C K E,FRIED I,et al. Invasive recordings from the human brain:Clinical insights and beyond[J]. Nature Reviews Neuroscience,2005,6(1):35.
[11] DONOGHUE J P. Bridging the brain to the world:A perspective on neural interface systems[J]. Neuron,2008,60(3):511-521.
[12] FARINA D,JIANG N,REHBAUM H,et al. The extraction of neural information from the surface EMG for the control of upper-limb prostheses:Emerging avenues and challenges[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2014,22(4):797-809.
[13] HARGROVE L J,SIMON A M,YOUNG A J,et al. Robotic leg control with EMG decoding in an amputee with nerve transfers[J]. New England Journal of Medicine,2013,369(13):1237-1242.
[14] HUANG H,ZHANG F,HARGROVE L J,et al. Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion[J]. IEEE Transactions on Biomedical Engineering,2011,58(10):2867-2875.
[15] SINGH R M,CHATTERJI S,KUMAR A. Trends and challenges in EMG based control scheme of exoskeleton robots-a review[J]. International Journal of Scientific and Engineering Research,2012,3(9):933-940.
[16] FLEISCHER C,HOMMEL G. A human——exoskeleton interface utilizing electromyography[J]. IEEE Transactions on Robotics,2008,24(4):872-882.
[17] KAWAMOTO H,TAAL S,NINISS H,et al. Voluntary motion support control of robot suit HAL triggered by bioelectrical signal for hemiplegia[C]//Engineering in Medicine and Biology Society (EMBC),2010 Annual International Conference of the IEEE. IEEE,2010:462-466.
[18] KIGUCHI K,HAYASHI Y. An EMG-based control for an upper-limb power-assist exoskeleton robot[J]. IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),2012,42(4):1064-1071.
[19] HOWARD M,BRAUN D J,VIJAYAKUMAR S. Transferring human impedance behavior to heterogeneous variable impedance actuators[J]. IEEE Transactions on Robotics,2013,29(4):847-862.
[20] AJOUDANI A. Transferring human impedance regulation skills to robots[M]. Berlin:Springer,2016.
[21] ISON M,VUJAKLIJA I,WHITSELL B,et al. High-density electromyography and motor skill learning for robust long-term control of a 7-DoF robot arm[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2016,24(4):424-433.
[22] SENSINGER J W,LOCK B A,KUIKEN T A. Adaptive pattern recognition of myoelectric signals:Exploration of conceptual framework and practical algorithms[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2009,17(3):270-278.
[23] YOUNG A J,HARGROVE L J,KUIKEN T A. The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift[J]. IEEE Transactions on Biomedical Engineering,2011,58(9):2537-2544.
[24] MARIEB E N,HOEHN K. Human anatomy & physiology[M]. Pearson Education,2007.
[25] SHI J,ZHENG Y P,HUANG Q H,et al. Continuous monitoring of sonomyography,electromyography and torque generated by normal upper arm muscles during isometric contraction:Sonomyography assessment for arm muscles[J]. IEEE Transactions on Biomedical Engineering,2008,55(3):1191-1198.
[26] ZHENG E,WANG L,WEI K,et al. A noncontact capacitive sensing system for recognizing locomotion modes of transtibial amputees[J]. IEEE Transactions on Biomedical Engineering,2014,61(12):2911-2920.
[27] ZHENG E,WANG Q. Noncontact capacitive sensing-based locomotion transition recognition for amputees with robotic transtibial prostheses[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2017,25(2):161-170.
[28] CAPPELLINI G,IVANENKO Y P,POPPELE R E,et al. Motor patterns in human walking and running[J]. Journal of Neurophysiology,2006,95(6):3426-3437.
[29] WANG Q,YUAN K,ZHU J,et al. A robotic transtibial prosthesis with nonlinear damping behaviors for terrain adaptation[J]. IEEE Robotics and Automation Magazine,2015(1):375-379.
[30] GAO W,EMAMINEJAD S,NYEIN H Y Y,et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis[J]. Nature,2016,529(7587):509.
[31] KALTENBRUNNER M,SEKITANI T,REEDER J,et al. An ultra-lightweight design for imperceptible plastic electronics[J]. Nature,2013,499(7459):458.
[32] KIM M,ALROWAIS H,PAVLIDIS S,et al. Size-scalable and high-density liquid-metal-based soft electronic passive components and circuits using soft lithography[J]. Advanced Functional Materials,2017,27(3):1604466
[33] BURDET E,OSU R,FRANKLIN D W,et al. The central nervous system stabilizes unstable dynamics by learning optimal impedance[J]. Nature,2001,414(6862):446.

基于非接触式电容传感的人体运动意图识别

Noncontact Capacitive Sensing Based Human Motion Intent Recognition

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