Art of Recognition the Electromyographic Signals for Control of the Bionic Artificial Limb of the Hand
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The LLC Bionic Natali company is a startup and has been engaging in creation of bionic artificial limbs of hands for more than 2 years. From the first steps, the project had been directed on the solution of a problem of development of the domestic bionic functional artificial limb of the hand based on neural network and others algorithms. In the project it had been created the functional system of management, system of tactile feedback which has increased controllability of a functional artificial limb is already realized and integrated, and also the functional bionic artificial limb of the hand. Based on this work it had been done the general representations and practical application of machine training, neural network and others algorithms. The technology of recognition of gestures of electromyographic activity based on neural network or an analog of network is the cornerstone. The bracelet is put on a hand (in case of disabled people, a stump), further noninvasive electrodes remove potential difference of neuromuscular activity; by means of an electric circuit there is data handling and their transmission to the processor where by means of a neural network there is a recognition of a gripper, further data are transferred for control of a bionic hand. Article belongs to the sections Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI).
KeywordsThe bionic artificial limb Neuronal net Electromyographic signals System of control EMG Bionic Natali Recognition the electromyographic signals Artificial Intelligence Machine learning
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- 1.M. Sh. Haziakhmetov. Properties of window dispersion of a myogramma as casual process. Systems and means inform., 2014, volume 24, release 3, page 110-120.Google Scholar
- 2.Englehart K, Hudgins B: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 2003, 50(7):848-854.Google Scholar
- 3.Van den Broek EL, Lis’y V, Janssen JH, Westerink JHDM, Schut MH, Tuinenbreijer K: Affective Man–machine Interface: Unveiling human emotions through biosignals. Biomedical Engineering Systems and Technologies: Communications in Computer and Information Science. Berlin, Germany: Springer Verlag; 2010.Google Scholar
- 4.Ebenezer Obaloluwa Olaniyi, Oyebade Kayode Oyedotun, Khashman Adnan,”Heart Diseases Diagnosis Using Neural Networks Arbitration”, IJISA, vol.7, no.12, pp.75-82, 2015. DOI: 10.5815/ijisa.2015.12.08.
- 5.M. A. H. Akhand, Mahtab Ahmed, M. M. Hafizur Rahman,”Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition”, International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.9, pp.40-50, 2016.DOI: 10.5815/ijigsp.2016.09.06.