Noncontact Capacitive Sensing Based Human Motion Intent Recognition

doi:10.3901/JME.2019.11.019

Abstract

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

CLC Number:

TG156

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.

References

[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.