Fingers Movements Control System Based on Artificial Neural Network Model
- 8 Downloads
Surface electromyographic (sEMG) signal is used in the various fields of applications where the need exists to measure the activity of body muscles, such as brain-computer interfaces, game industry, medical engineering, and other practical spheres. Even more, the use of sEMG signal in the field of active prosthesis industry has become traditional for many years. However, despite the fact that the question of using it in the field of fingers prostheses is still open, in general, the sEMG signal required multichannel measuring devices or massive, voluminous equipment for precise recognition of hands or fingers movement. That is decreasing the possible portability and convenience of prostheses and as a consequence is increasing their final price. In this paper we propose a method of organizing the controlling and measuring unit of the prosthetic device based on artificial neural network (ANN) model and one-channel microcontroller based sEMG measuring system. The proposed ANN model works with only 4 input time-domain features of sEMG signal and provides an accuracy of 95.52% for classification of 6 different types of finger movements that makes it a good solution for next implementation in the system of prosthetic fingers or wrist devices.
Unable to display preview. Download preview PDF.
- 6.S. G. Postema, R. M. Bongers, M. A. Brouwers, H. Burger, L. M. Norling–Hermansson, M. F. Reneman, P. U. Dijkstra, C. K. Van der Sluis, “Upper limb absence: predictors of work participation and work productivity,” Arch. Phys. Med. Rehabil. 97, 892 (2016). DOI: https://doi.org/10.1016/j.apmr.2015.12.022.CrossRefGoogle Scholar
- 13.J. Koprnicky, P. Najman, J. Safka, “3D printed bionic prosthetic hands,” Proc. of 2017 IEEE Int. Workshop on Electronics, Control, Measurement, Signals and their Application to Mechatronics, ECMSM, 24–26 May 2017, Donostia–San Sebastian, Spain (IEEE, 2017), pp. 1–6. DOI: https://doi.org/10.1109/ECMSM.2017.7945898.Google Scholar
- 15.B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci, “The psychophysiology primer: a guide to methods and a broad review with a focus on human–computer interaction,” in: Foundations and Trends in Human–Computer Interaction 9, No. 3–4, 150 (2016). DOI: https://doi.org/10.1561/1100000065.Google Scholar
- 16.W. Ma, X. Zhang, G. Yin, “Design on intelligent perception system for lower limb rehabilitation exoskeleton robot,” Proc. of IEEE 13th Int. Conf. on Ubiquitous Robot and Ambient Intelligence, 19–22 Aug 2016, Xian, China (IEEE, 2016), pp. 587–592. DOI: https://doi.org/10.1109/URAI.2016.7625785.Google Scholar
- 21.M. Ariyanto, W. Caesarendra, K. A. Mustaqim, M. Irfan, J. A. Pakpahan, J. D. Setiawan, A. R. Winoto, “Finger movement pattern recognition method using artificial neural network based on electromyography (EMG) sensor,” Proc. of Int. Conf. on Automation, Cognitive Science, Optics, Micro Electro–Mechanical System, and Information Technology, ICACOMIT, 29–30 Oct 2015, Bandung, Indonesia (IEEE, 2015), pp. 12–17. DOI: https://doi.org/10.1109/ICACOMIT.2015.7440146.Google Scholar
- 23.M. Zecca, S. Micera, M. C. Carrozza, P. Dario, “Control of multifunctional prosthetic hands by processing the electromyographic signal,” Crit. Rev. Biomed. Eng. 30, No. 4–6, 459 (2002). DOI: https://doi.org/10.1615/CritRevBiomedEng.v30.i456.80.
- 27.K. P. Vonsevych, M. O. Bezuglyi, A. O. Haponiuk, “Information–measuring system of myograph of bionic limb prosthesis,” Perspectyvni Tekhnologii ta Prylady 10, 32 (2017).Google Scholar
- 28.M. Heiderich, S. Leonhardt, W. Krantz, J. Neubeck, J. Wiedemann, “Method for analysing the feeling of safety at high speed using virtual test drives,” Proc. of 18 Internationales Stuttgarter Symp. (Springer Vieweg, Wiesbaden, 2018), pp. 875–886. DOI: https://doi.org/10.1007/978-3-658-21194-3_67.Google Scholar