Advertisement

A soft robotic hand: design, analysis, sEMG control, and experiment

  • Naishi Feng
  • Qiurong Shi
  • Hong WangEmail author
  • Jiale Gong
  • Chong Liu
  • Zhiguo Lu
ORIGINAL ARTICLE

Abstract

Soft robot is a new type of flexible robot which can imitate human hand activity. Electromyographic (EMG) signal is an important bioelectrical signal associated with muscle activity. The innovative combination of soft robot and EMG shows great potential. Based on this inspiration, a humanoid soft robotic hand controlled by EMG was proposed. We designed a single finger 3D model for the soft robotic hand and put forward the three-stage cavity structure. The finite element analysis has been performed to obtain the influence of the geometrical parameters including the number of cavities, the shape of the cavity side section, and the pressure in the cavity on the single finger bending performance. The optimal geometrical parameters were obtained. We analyzed the geometrical deformation of the finger simulation model and figured out the relationship between the input pressure of the soft hand and the angle of bending deformation. In addition, we designed and manufactured the soft robotic hand model and its pneumatic system. Twenty-four effective eigenvalues were extracted from the surface EMG signal (sEMG) of the forearm muscle group and ten-kinds-gestures recognizing system was established. Finally, we realized the online sEMG control of the soft robotic hand, so that the soft robotic hand can reproduce the gestures behavior of human. The correct rate of recognition is 96%. Conclusions obtained in this paper provide theoretical support for the development of humanoid soft robotic hand.

Keywords

Soft robot sEMG Three-segment cavity structure Finite element analysis Human-robot interaction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Funding information

This study was supported by the National Natural Science Foundation of China (51505069, 51405073), National Key R & D Program of China (2017YFB1300300), the University Innovation Team of Liaoning Province (LT2014006), and the Fundamental Research Funds for the Central Universities (N150308001).

References

  1. 1.
    Rus D, Tolley MT (2015) Design, fabrication and control of soft robots. Nature 521(7553):467–475CrossRefGoogle Scholar
  2. 2.
    Nemitz MP, Mihaylov P, Barraclough TW, Ross D, Stokes AA (2016) Using voice coils to actuate modular soft robots: wormbot, an example. Soft Robot 3(4):198–204CrossRefGoogle Scholar
  3. 3.
    Kim DH, Rogers JA (2010) Stretchable electronics: materials strategies and devices & dagger. Adv Mater 20(24):4887–4892CrossRefGoogle Scholar
  4. 4.
    Ross D, Nemitz MP, Stokes AA (2016) Controlling and simulating soft robotic systems: insights from a thermodynamic perspective. 3 (4)Google Scholar
  5. 5.
    Case JC, White EL, Kramer RK (2015) Soft Material Characterization for Robotic Applications 2 (2):80–87Google Scholar
  6. 6.
    Russo S, Ranzani T, Liu H, Nefti-Meziani S, Althoefer K, Menciassi A (2015) Soft and stretchable sensor using biocompatible electrodes and liquid for medical applications. Soft Robot 2(4):146–154CrossRefGoogle Scholar
  7. 7.
    Manti M, Hassan T, Passetti G, D’Elia N, Laschi C, Cianchetti M (2015) A bioinspired soft robotic gripper for adaptable and effective grasping. 2(3)Google Scholar
  8. 8.
    Klute GK, Czerniecki JM, Hannaford B (1999) McKibben artificial muscles: pneumatic actuators with biomechanical intelligence. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 1999. Proceedings. pp 221–226Google Scholar
  9. 9.
    Deimel R, Brock O (2016) A novel type of compliant and underactuated robotic hand for dexterous grasping. Sage Publications, Inc.Google Scholar
  10. 10.
    Galloway KC, Polygerinos P, Walsh CJ, Wood RJ(2016) Mechanically programmable bend radius for fiber-reinforced soft actuators. In: International Conference on Advanced Robotics. pp 1–6Google Scholar
  11. 11.
    Polygerinos P, Wang Z, Galloway KC, Wood RJ, Walsh CJ (2015) Soft robotic glove for combined assistance and at-home rehabilitation. Robot Auton Syst 73(C):135–143CrossRefGoogle Scholar
  12. 12.
    Homberg BS, Katzschmann RK, Dogar MR, Rus D (2015) Haptic identification of objects using a modular soft robotic gripper. In: Ieee/rsj International Conference on Intelligent Robots and Systems. pp 1698–1705Google Scholar
  13. 13.
    Morrow J, Shin HS, Phillips-Grafflin C, Jang SH, Torrey J, Larkins R, Dang S, Park YL, Berenson D (2016) Improving soft pneumatic actuator fingers through integration of soft sensors, position and force control, and rigid fingernails. In: IEEE International Conference on Robotics and Automation. pp 5024–5031Google Scholar
  14. 14.
    Connolly F, Walsh CJ, Bertoldi K (2017) Automatic design of fiber-reinforced soft actuators for trajectory matching. Proc Natl Acad Sci U S A 114(1):51–56CrossRefGoogle Scholar
  15. 15.
    Donaldson S, Donaldson M, Snelling L (2003) SEMG evaluations: an overview. Appl Psychophysiol Biofeedback 28(2):121–127CrossRefGoogle Scholar
  16. 16.
    Hogrel JY, Duchêne J, Marini JF (1998) Variability of some SEMG parameter estimates with electrode location. J Electromyogr Kinesiol Off J Int Soc Electrophysiol Kinesiol 8(5):305–315CrossRefGoogle Scholar
  17. 17.
    Jian W (2000) Some advances in the research of sEMG signal analysis and its application. SportenceGoogle Scholar
  18. 18.
    Saponas TS, Tan DS, Dan M, Balakrishnan R (2008) Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces. In: Sigchi Conference on Human Factors in Computing Systems . pp 515–524Google Scholar
  19. 19.
    Saponas TS, Tan DS, Dan M, Turner J, Landay JA (2010) Making muscle-computer interfaces more practical. In: Sigchi Conference on Human Factors in Computing Systems. pp 851–854Google Scholar
  20. 20.
    Englehart K, Hudgins B, Parker PA (2001) A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 48(3):302–311CrossRefGoogle Scholar
  21. 21.
    Zhao Z, Chen X, Zhang X, Yang J, Tu Y, Lantz V, Wang K (2007) Study on online gesture sEMG recognitionGoogle Scholar
  22. 22.
    Khezri M, Jahed M (2011) A neuro–fuzzy inference system for sEMG-based identification of hand motion commands. NeuroImage 108(108):60–73Google Scholar
  23. 23.
    Polygerinos P, Lyne S, Wang Z, Nicolini LF, Mosadegh B, Whitesides GM, Walsh CJ (2014) Towards a soft pneumatic glove for hand rehabilitation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. pp 1512–1517Google Scholar
  24. 24.
    Yang Y, Chen Y, Li Y, Michael ZQC, Wei Y (2017) Bioinspired robotic fingers based on pneumatic actuator and 3D printing of smart material. 4(2)Google Scholar
  25. 25.
    Huang JL (2008) FEA of hyperelastic rubber material based on Mooney-Rivlin model and Yeoh model. China Rubber Indust 8:467–471Google Scholar
  26. 26.
    Polygerinos P, Mosadegh B, Campo A (2007) PneuNets bending actuators Publishing PhysicsWeb. https://softroboticstoolkit.com/book/pneunets-bending-actuator. Accessed 28 Feb 2007
  27. 27.
    Dickie JA, Faulkner JA, Barnes MJ, Lark SD (2017) Electromyographic analysis of muscle activation during pull-up variations. J Electromyogr Kinesiol Off J Int Soc Electrophysiol Kinesiol 32:30–36CrossRefGoogle Scholar
  28. 28.
    Netter FH, Craig JA, Perkins J (2002) Atlas of neuroanatomy and neurophysiology. USAGoogle Scholar
  29. 29.
    Kumar DK, Melaku A (2473) Electrode distance and magnitude of SEMG. In: Engineering in medicine and biology, 2002. Conference and the Fall Meeting of the Biomedical Engineering Society Embs/bmes Conference, 2002. Proceedings of the second joint, 2002. Pp 2477–2480 volGoogle Scholar
  30. 30.
    Micera S, Sabatini AM, Dario P, Rossi B (1999) A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques. Med Eng Phys 21(5):303–311CrossRefGoogle Scholar
  31. 31.
    Zhang X, Chen X, Wang WH, Yang JH, Wang KQ, Wang KQ (2009) Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors. In: International Conference on Intelligent User Interfaces. pp 401–406Google Scholar
  32. 32.
    Zhang X, Chen X, Zhao ZY, Li Q, Yang JH, Lantz V, Wang KQ(2008) An adaptive feature extractor for gesture SEMG recognition. In: International Conference on Medical Biometrics. pp 83–90Google Scholar
  33. 33.
    Petruccelli JD, Woolford SW (1984) A threshold AR(1) model. J Appl Probab 21(2):270–286MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Shang X, Tian Y, Li Y (2011) Feature extraction and classification of sEMG based on ICA and EMD decomposition of AR model. In: International Conference on Electronics, Communications and Control. pp 1464–1467Google Scholar
  35. 35.
    Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666CrossRefGoogle Scholar
  36. 36.
    Cantarella V (1999) Bones and muscles—an illustrated anatomy. South Westerlo, New YorkGoogle Scholar
  37. 37.
    Yankun Z, Chongqing L (2003) A novel face recognition method based on linear discriminant analysis. J Infrared Millimeter Waves 22(5):327–330Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Naishi Feng
    • 1
  • Qiurong Shi
    • 1
  • Hong Wang
    • 1
    Email author
  • Jiale Gong
    • 1
  • Chong Liu
    • 1
  • Zhiguo Lu
    • 1
  1. 1.Department of mechanical engineering and automationNortheastern UniversityShenyang CityPeople’s Republic of China

Personalised recommendations