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Innovation

Electromyography pattern-recognition based prosthetic limb control using various machine learning techniques

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Pages 370-377 | Received 27 Sep 2020, Accepted 30 Mar 2022, Published online: 20 Apr 2022

References

  • Gayathri G, Udupa G, Nair GJ. Control of bionic arm using ICA-EEG. Paper presented at the International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT); 2017 July 6–7; Kerala, India.
  • Manna S, Ghildiyal S, Bhimani K. Face recognition from video using deep learning. Paper presented at the 5th International Conference on Communication and Electronics Systems (ICCES); 2020 June 10–12; Coimbatore, India.
  • Ali EAH, Mohamed AYS. Modeling of human hand fingers states using electromyography and logistic regression. Proc 2016 Conf Basic Sci Eng Stud. 2016;3:38–42.
  • Côtéallard U, Nougarou F, Fall CL, et al. A convolutional neural network for robotic arm guidance using sEMG based frequency-features. Paper presented at the IEEE International Conference on Intelligent Robots and Systems, 2016 October 9–14; Daejeon, Korea.
  • Bodiwala D, Summerton DJ, Terry TR. Testicular prostheses: development and modern usage. Ann R Coll Surg Engl. 2007;89(4):349–353.
  • Sherstan C, Modayil J, Pilarski PM. A collaborative approach to the simultaneous multi-joint control of a prosthetic arm. Paper presented at the IEEE International Conference on Rehabilitation Robotics; 2015 August 11–14; Singapore.
  • Fifer MS, Acharya S, Benz HL, et al. Toward electrocorticographic control of a dexterous upper limb prosthesis: building brain-machine interfaces. IEEE Pulse. 2012;3(1):38–42.
  • Ficuciello F, Pisani G, Marcellini S, et al. The PRISMA hand I: a novel underactuated design and EMG/voice-based multimodal control. Eng Appl Artif Intell. 2020;93:103698.
  • Tavakoli M, Benussi C, Lourenco JL. Single channel surface EMG control of advanced prosthetic hands: a simple, low cost and efficient approach. Expert Syst Appl. 2017;79:322–332.
  • Fifer MS, Hotson G, Wester BA, et al. Simultaneous neural control of simple reaching and grasping with the modular prosthetic limb using intracranial EEG. IEEE Trans Neural Syst Rehabil Eng. 2014;22(3):695–705.
  • Benchabane SI, Saadia N, Ramdane-Cherif A. Novel algorithm for conventional myocontrol of upper limbs prosthetics. Biomed Signal Process Control. 2020;57:101791.
  • Vasuki R. Modeling of prosthetic limb rotation control by sensing rotation of residual arm bone. Int J Pharm Technol. 2015;7(2):8761–8771.
  • Engeberg ED, Meek S. Improved grasp force sensitivity for prosthetic hands through force-derivative feedback. IEEE Trans Biomed Eng. 2008;55(2):817–821.
  • Prakash A, Sahi AK, Sharma N, et al. Force myography controlled multifunctional hand prosthesis for upper-limb amputees. Biomed Signal Process Control. 2020;62:102122.
  • Mukhopadhyay AK, Samui S. An experimental study on upper limb position invariant EMG signal classification based on deep neural network. Biomed Signal Process Control. 2020;55:101669.
  • 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. IEEE Trans Neural Syst Rehabil Eng. 2014;22(4):797–809.
  • Wattanasiri P, Tangpornprasert P, Virulsri C. Design of multi-grip patterns prosthetic hand with single actuator. IEEE Trans Neural Syst Rehabil Eng. 2018;26(6):1188–1198.
  • Liu Y-H, Huang H-P, Weng C-H. Recognition of electromyographic signals using cascaded kernel learning machine. IEEE/ASME Trans Mechatron. 2007;12(3):253–264.
  • Krasoulis A, Kyranou I, Erden MS, et al. Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements. J NeuroEngineering Rehabil. 2017;14:71.
  • Scheme EJ, Hudgins BS, Englehart KB. Confidence-based rejection for improved pattern recognition myoelectric control. IEEE Trans Biomed Eng. 2013;60(6):1563–1570.
  • Castellini C, Gruppioni E, Davalli A, et al. Fine detection of grasp force and posture by amputees via surface electromyography. J Physiol Paris. 2009;103(3–5):255–262.
  • sparkfun.com [Internet]. USA; [cited 2018 Dec 10]. Available from: https://cdn.sparkfun.com/datasheets/Sensors/Biometric/MyowareUserManualAT-04-001.pdf.
  • Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297.
  • limbless-association.org [Internet]. UK; [cited 2018 Dec 15]. Available from: https://limbless-association.org/2021/02/03/amplafy-episode-1/.

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