FPGA based mod-controlled prosthetic arm control with EMG signal

Yıl 2022, Cilt: 12 Sayı: 1, 90 - 100, 15.01.2022

Uğur Can Boz , Cengiz Tepe , İdris Sancaktar

https://doi.org/10.17714/gumusfenbil.972636

Öz

Nowadays, Electromyogram (EMG) signals are used for many purposes such as the detection of muscle diseases, rehabilitation devices, prostheses, orthoses and facilitating human life. However, in real-time prosthetic arm applications, these systems must have a compact structure and offer optimum solutions in terms of power consumption and time in order to be portable. In this study, Field Programmable Gate Array (FPGA) based, real-time and mode-controlled prosthetic arm control was performed using 2-channel EMG signals. By contracting two muscles together without the need for an additional sensor, switching between different modes is provided. ADC conversion of EMG signal, 2 Hz low pass FIR filter, thresholding and PWM operations are performed in the FPGA board. In addition, the data obtained at each stage was monitored in real-time on the computer. As a result of the study, 98% accuracy was achieved in the prosthetic arm movements and 99% in the mode change process.

Anahtar Kelimeler

EMG, FIR filter, FPGA, Mode control, Prosthetic arm

Proje Numarası

yok

EMG işareti ile FPGA tabanlı mod denetimli protez kol kontrolü

Öz

Günümüzde elektromiyogram (EMG) işaretleri kas hastalıklarının tespiti, rehabitilasyon cihazları, protezler, ortezler ve insan hayatını kolaylaştırmak gibi birçok amaç ile kullanılmaktadır. Ancak gerçek zamanlı protez kol uygulamalarında, bu sistemlerin taşınabilir yapıda olması için hem kompakt bir yapıya sahip olması hem de güç tüketimi ve süre açısından optimum çözümler sunması gerekmektedir. Bu çalışmada, 2 kanallı EMG işaretleri kullanılarak Alanda Programlanabilir Kapı Dizisi (FPGA) tabanlı, gerçek zamanlı ve mod denetimli protez kol kontrolü gerçekleştirilmiştir. Ek bir sensöre ihtiyaç duymadan iki kasın birlikte kasılması ile farklı modlar arasında geçiş sağlanmıştır. FPGA kartı içerisinde EMG işaretinin ADC dönüşümü, 2 Hz alçak geçiren FIR süzgeç, eşikleme ve PWM işlemleri gerçekleştirilmiştir. Ayrıca her aşamada elde edilen veriler gerçek zamanlı olarak bilgisayar üzerinden izlenmesi sağlanmıştır. Çalışmanın sonucunda gerçekleştirilen protez kol hareketlerinde %98, mod değiştirme işleminin gerçekleştirilmesinde ise %99 doğruluk elde edilmiştir.

Anahtar Kelimeler

EMG, FIR süzgeç, FPGA, Mod denetimi, Protez kol

Destekleyen Kurum

yok

Proje Numarası

yok

Teşekkür

yok

Kaynakça

• Ayvali, M., Wickenkamp, I. and Ehrmann, A. (2021). Design, construction and tests of a low-cost myoelectric thumb. Technologies, 9(3), 63. https://doi.org/10.3390/technologies9030063
• Borbely, B. J., Kincses, Z., Voroshazi, Z., Nagy, Z. and Szolgay, P. (2014). A modular test platform for real-time measurement and analysis of EMG signals for improved prosthesis control. International Workshop on Cellular Nanoscale Networks and their Applications, 1-2. https://doi.org/10.1109/CNNA.2014.6888643
• Boschmann, A., Agne, A., Witschen, L., Thombansen, G., Kraus, F. and Platzner, M. (2015). FPGA-based acceleration of high density myoelectric signal processing. 2015 International Conference on Reconfigurable Computing and FPGAs, 123, 77–89. https://doi.org/10.1016/j.jpdc.2018.07.004
• Bu, N., Hamamoto, T., Tsuji, T. and Fukuda, O. (2004). FPGA implementation of a probabilistic neural network for a bioelectric human interface. The 2004 47th Midwest Symposium on Circuits and Systems, iii–29. https://doi.org/10.1109/MWSCAS.2004.1354283
• Caldwell, P., Al-Bayaty, R., Kellar, C. and Shin, I. (2012). Biomechanics: surface electromyography prosthesis control. 5th International Conference on BioMedical Engineering and Informatics (BMEI 2012), 786-789. https://doi.org/10.1109/BMEI.2012.6512954.
• Chabchoub, S., Mansouri, S. and Salah, R. B. (2015). Biomedical monitoring system using LabVIEW FPGA. 2015 World Congress on Information Technology and Computer Applications, 1-5. https://doi.org/10.1109/WCITCA.2015.7367020
• Chen, X., Ke, A., Ma, X. and He, J. (2016). SoC-based architecture for robotic prosthetics control using surface electromyography. 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, 1, 134–37. https://doi.org/10.1109/IHMSC.2016.31
• De Paula Felipe De Oliveira, J., Junior, E. A. and Roda, V. O. (2014). A reconfigurable control system using EMG. 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 1318–1321. https://doi.org/10.1109/I2MTC.2014.6860959
• Digilent Inc.: Basys 3 FPGA board reference manual power supplies. (2021, July 15). Retrieved from www.digilentinc.com
• Prakash, A. and Sharma, S. (2020). A low-cost system to control prehension force of a custom-made myoelectric hand prosthesis. Research on Biomedical Engineering, 36(3), 237–47. https://doi.org/10.1007/s42600-020-00064-w
• Siddiq Ahmed, S., Almusawi, A. R. J., Yilmaz, B. and Dogru, N. (2021). Design and multichannel electromyography system-based neural network control of a low-cost myoelectric prosthesis hand. Mechanical Sciences, 12(1), 69–83. https://doi.org/10.5194/ms-12-69-2021
• Sudharsan, R. R., Deny, J., Muthukumaran, E. and Selvi, S. C. (2020). Design, implementation, and estimation of MFCV for 4-different position of human body using FPGA. Microelectronics Journal, 105. https://doi.org/10.1016/j.mejo.2020.104890
• Sundaram, K., Marichamy and Pradeepa. (2016). FPGA based filters for EEG pre-processing. 2016 2nd International Conference on Science Technology Engineering and Management (ICONSTEM) (ss. 572–576). https://doi.org/10.1109/ICONSTEM.2016.7560958
• Tepe, C. ve Eminoğlu, İ. (2014). Düşük maliyetli mayo-elektrik denetimli protez el projesi. 16. Otomatik Kontrol Ulusal Toplantısı (ss. 657–662). Kocaeli.
• Tepe, C., Erdim, M. ve Eminoğlu, I. (2020). Myo bileklik ile gerçek zamanlı protez kol kontrolü. European Journal of Science and Technology, 184–93. https://doi.org/10.31590/ejosat.779672
• Thukral, R., Gulshan, M. and Tyagi, M. P. (2015). Hardware implementation to develop prosthetic hand-a review. International Journal of Engineering Development and Research, 3(3).
• Wöhrle, H., Tabie, M., Kim, S. K., Kirchner, F. and Kirchner, E. A. (2017). A hybrid FPGA-based system for EEG- and EMG-based online movement prediction. Sensors (Switzerland) 17(7). https://doi.org/10.3390/s17071552
• Wu, H., Dyson, M. and Nazarpour, K. (2021). Arduino-based myoelectric control: towards longitudinal study of prosthesis use. Sensors (Switzerland), 21(3), 1–13. https://doi.org/10.3390/s21030763
• Zhang, X., Huang, H. and Yang, Q. (2012). Implementing an FPGA system for real-time intent recognition for prosthetic legs. Proceedings - Design Automation Conference (ss. 169–175). New York: Association for Computing Machinery. https://doi.org/10.1145/2228360.2228394