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In this paper the use of a commercial EMG armband for the motion control of a prototype hand prosthesis is proposed. The mechanical design is based on an open source six degree-of-freedom hand. Some modifications from the original design are proposed, mainly in the actuation and power transmission devices to reduce the prototype's costs and to provide a major mobility to the thumb to adapt the motion to the shape of the grasped object. Unlike some similar prototypes previously reported and considering that the proposed application requires portability, the use of a PC for the acquisition and processing of the EMG data has been replaced by a portable hardware system based on the master/slave architecture. The master device is a Raspberry Pi-based subsystem interfaced with the EMG armlet for gathering and classifying information from the user's muscular activity. The slave device is an ATmega328 microcontroller-based subsystem that defines the movements of the robotic hand from the information collected and processed by the master device. Experimental results are presented to evaluate the performance of the EMG-controlled hand prosthesis carrying out different types of grasping tasks.
Twórcy
  • Universidad Tecnológica de la Mixteca, Institute of Electronics and Mechatronics
  • Universidad Tecnológica de la Mixteca, Institute of Electronics and Mechatronics
  • Universidad Tecnológica de la Mixteca, Institute of Electronics and Mechatronics
  • Universidad Tecnológica de la Mixteca, Institute of Electronics and Mechatronics
Bibliografia
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  • [3] Ottobock, https://www.ottobock.com.us/media/pdf/ 2013_Prosthetics_Upper_Limb_Global_Catalog.pdf (December, 2017).
  • [4] Ottobock, https://www.ottobockus.com/media/localmedia/ prosthetics/upper-limb/michelangelo/files/ michelangelobrochure.pdf (December, 2017).
  • [5] P.O.S. International, prostheticsolutions.com/bebionic/ (2019).
  • [6] V. Systems, https://vincentsystems.de/en/prosthetics/ vincent-evolution-3/ 2019.
  • [7] Bennett DA, Dalley SA, Truex D, Goldfarb M. A multigrasp hand prosthesis for providing precision and conformal grasps. IEEE/ASME Trans Mechatronics 2015;20(4):1697–704. http://dx.doi.org/10.1109/TMECH.2014.2349855.
  • [8] Lenzi T, Lipsey J, Sensinger JW. The ric arm a small anthropomorphic transhumeral prosthesis. IEEE/ASME Trans Mechatronics 2016;21(6):2660–71. http://dx.doi.org/10.1109/TMECH.2016.2596104.
  • [9] Krausz NE, Rorrer RA, Weir RF. Design and fabrication of a six degree-of-freedom open source hand. IEEE Trans Neural Syst Rehabil Eng 2016;24(5):562–72. http://dx.doi.org/10.1109/TNSRE.2015.2440177.
  • [10] Baspinar U, Barol HS, Senyurek VY. Performance comparison of artificial neural network and gaussian mixture model in classifying hand motions by using semg signals. Biocybern Biomed Eng 2013;33(1):33–5. http://dx.doi.org/10.1016/S0208-5216(13)70054-8.
  • [11] Barabulut D, Ortes F, Arslan YZ, Adli MA. Comparative evaluation of emg signals features for myoelectric controlled human arm prosthetics. Biocybern Biomed Eng 2017;37(2):326–35. http://dx.doi.org/10.1016/j.bbe.2017.03.001.
  • [12] Hakonen M, Piitulainen H, Visala A. Current state of digital signal processing in myoelectric interfaces and related applications. Biomed Signal Process Control 2015;18:334–59. http://dx.doi.org/10.1016/j.bspc.2015.02.009.
  • [13] Russo RE, Fernandez JG, Rivera RR, Kuzman MG, Lopez JM, Gemin WA, Revuelta MA. Algorithm of myoelectric signals processing for the control of prosthetic robotic hands. J Com Sci Tech 2018;18(1):28–34. http://dx.doi.org/10.24215/16666038.18.e04.
  • [14] Wan-Ting S, Zong-Jhe L, Shih-Tsang T, Tsorng-Lin C, Chia- Yen Y. A bionic hand controlled by hand gesture recognition based on surface emg signals: A preliminary study. Biocybern Biomed Eng 2018;38(1):126–35. http://dx.doi.org/10.1016/j.bbe.2017.11.001.
  • [15] Wang N, Lao K, Zhang X. Design and myoelectric control of an anthropomorphic prosthetic hand. J Bionic Eng 2017;14 (1):47–59. http://dx.doi.org/10.1016/S1672-6529(16)60377-3.
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  • [18] Wiste TE, Dalley SA, Varol HA, Goldfarb M. Design of a multigrasp transradial prosthesis. ASME J Med Devices 2011;5(3):1–7. http://dx.doi.org/10.1115/1.4004653.
  • [19] Ding J, Lin RZ, Lin ZY. Service robot system with integration of wearable Myo armband for specialized hand gesture human-computer interfaces for people with disabilities with mobility problems. Comput Electr Eng 2018;69:815–27. http://dx.doi.org/10.1016/j.compeleceng.2018.02.041.
  • [20] Boyali A, Hashimoto N. Spectral collaborative representation based classification for handgestures recognition on electromyography signals. Biomed Signal Process Control 2016;24:11–8. http://dx.doi.org/10.1016/j.bspc.2015.09.001.
  • [21] Phinyomarka A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst Appl 2012;39(8):7420–31. http://dx.doi.org/10.1016/j.eswa.2012.01.102.
  • [22] Subasi A. Classification of EMG signals using pso optimized svm for diagnosis of neuromuscular disorders. Comput Biol Med 2013;43(5):576–86. http://dx.doi.org/10.1016/j.compbiomed.2013.01.020.
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  • [24] Irastorza-Landa N, Sarasola-Sanz A, Shiman F, Lopez- Larraz E, Klein J, Valencia D, Belloso A, Morin FO, Birbaumer N, Ramos-Murguialday A. EMG discrete classification towards a myoelectric control of a robotic exoskeleton in motor rehabilitation. In: Ibáñez J, Gonzalez-Vargas J, Azorin JM, Akay M, Pons JL, editors. Converging Clinical and Engineering Research on Neurorehabilitation II. Springer; 2017.
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  • [26] Sanchez-Velasco LE, Arias-Montiel M, Guzman-Ramirez E. Emg data of the myo armband. Mendeley Data 2019;V1:1. http://dx.doi.org/10.17632/rwbs7645hg.1.
  • [27] L.E. Sanchez, https://www.youtube.com/watch? v=Re-yPQohrnY&feature=youtu.be (2019).
  • [28] Arias M, Sanchez LE. Experimental grasping forces for robotic hand. Mendeley Data 2019;V2:1. http://dx.doi.org/10.17632/hpxw32n6xs.1.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-ee6bf19f-13ee-48b7-a813-b9a16ac2d30d
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