Real Time Prosthesis Arm Control With Myo Armband

Abstract

The use of wearable devices, which provide perception of movements in the human body, is increasing with the development of technology. Within the scope of the study, prosthetic arm control was performed in real time with the myo armband produced by Thalmic Labs. As a result of contraction of the muscles in the human arm, EMG (electromyogram) and IMU (Inertial Measurement Unit) data were obtained with sensors on the myo wrist. With these data, four hand motion class information was created. This class information was directly transferred from the myo wristband to the arduino with the HM 10 Bluetooth Module. In this study, for the first time, switching mode method was used for hand, wrist, elbow and shoulder control for the prosthetic arm. It is important to perform a very small number of prosthetic arm movements with EMG data obtained from a small number of muscles due to the insufficient number of effective muscles. The system created in this study is a solution to these movement restrictions in amputated people.

Keywords

• Electromyogram (EMG)
• Prosthetic Arm
• Myo Armband
• Switching Mode Method

Myo Bileklik İle Gerçek Zamanlı Protez Kol Kontrolü

Abstract

İnsan vücudundaki hareketleri algılamayı sağlayan giyilebilir cihazların kullanımı teknolojinin gelişmesi ile birlikte artmaktadır. Çalışma kapsamında Thalmic Labs tarafından üretilen myo kol bandı ile protez kol kontrolü gerçek zamanlı olarak gerçekleştirilmiştir. İnsan kolundaki kasların kasılması sonucunda myo bileklikteki sensorlar ile EMG (elektromiyogram) ve IMU(Atalet Ölçüm Ünitesi) verileri alınmıştır. Bu veriler ile dört el hareket sınıf bilgisi oluşturulmuştur. Bu sınıf bilgileri HM 10 Bluetooth Modülü ile myo bileklikten arduinoya doğrudan aktarılmıştır. Bu çalışmada protez kol için el, bilek, dirsek ve omuz kontrolü için ilk kez anahtarlama mod yöntemi kullanılmıştır. Ampute bireylerde etkin kas sayısı yetersiz olduğundan dolayı az sayıda olan kaslardan alınan EMG verileri ile çok az sayıda protez kol hareketleri denetiminin gerçekleştirilmesi önemlidir. Bu çalışmada oluşturulan sistem ampute bireylerdeki bu hareket kısıtlamalarına bir çözüm niteliğindedir.

Keywords

• Elektromiyogram (EMG)
• Protez Kol
• Myo Kol Bandı
• Anahtarlama Mod Yöntemi

References

1. Abraham, Z., Kwon, D. B., Solomon, T., Xie, M., & Yeh, K. (2015). Control of an affordable hand and wrist prosthesis. Paper presented at the 15th Research Symposium, Rutgers School of Engineering.
2. Boyali, A., & Hashimoto, N. (2016). Spectral Collaborative Representation based Classification for hand gestures recognition on electromyography signals. Biomedical Signal Processing Control, 24, 11-18.
3. Cacace, J., Finzi, A., & Lippiello, V. (2016). Multimodal interaction with multiple co-located drones in search and rescue missions. arXiv preprint arXiv:1605.07316.
4. Cognolato, M., Atzori, M., Faccio, D., Tiengo, C., Bassette, F., Gassert, R., & Muller, H. (2018). Hand Gesture Classification in Transradial Amputees Using the Myo Armband Classifier. Paper presented at the 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob).
5. Cognolato, M., Atzori, M., Marchesini, C., Marangon, S., Faccio, D., Tiengo, C., . . . Müller, H. (2018). Multifunction control and evaluation of a 3D printed hand prosthesis with the Myo armband by hand amputees. BioRxiv, 445460.
6. Çoban, M., & Gelen, G. (2018). Wireless teleoperation of an industrial robot by using myo arm band. Paper presented at the 2018 International Conference on Artificial Intelligence and Data Processing (IDAP).
7. Erin, K., & Boru, B. (2018). EMG ve jiroskop verileri ile endüstriyel robot kolunun gerçek zamanlı kontrolü. Sakarya University Journal of Science, 22(2), 509-515.
8. Ganiev, A., Shin, H.-S., & Lee, K.-H. (2016). Study on virtual control of a robotic arm via a myo armband for the selfmanipulation of a hand amputee. Int. J. Appl. Eng. Res, 11(2), 775-782.

9. Gelen, G., & Özcan, S. (2019). İnsan-robot etkileşiminin biyomimetik yaklaşımla sağlanması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25(2), 188-198.
10. Hassan, H. F., Abou-Loukh, S. J., & Ibraheem, I. K. (2019). Teleoperated robotic arm movement using electromyography signal with wearable Myo armband. Journal of King Saud University-Engineering Sciences.
11. Heerschop, A., van der Sluis, C. K., Otten, E., & Bongers, R. M. (2020). Looking beyond proportional control: The relevance of mode switching in learning to operate multi-articulating myoelectric upper-limb prostheses. Biomedical Signal Processing Control, 55, 101647.
12. Klein, B. (2015). A Gesture Control Framework Targeting High-Resolution Video Wall Displays. Paper presented at the 2015 19th International Conference on Information Visualisation.
13. Kristof, R., Moldovan, C., Ciupe, V., Maniu, I., & Banda, M. (2019). Applications based on electromyography sensors. Paper presented at the ITM Web of Conferences. Langevin, G. (20.02.2020). Prosthetic Hand. from http://inmoov.fr/
14. Lopes, J., Simão, M., Mendes, N., Safeea, M., Afonso, J., & Neto, P. (2017). Hand/arm gesture segmentation by motion using IMU and EMG sensing. Procedia Manufacturing, 11, 107-113.
15. Moneada, A., Satizabal, D., Hoyos, G., & Padilla, B. (2017). Integration of a prototype of bionic prosthesis hand. Paper presented at the 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC).
16. Morais, G. D., Neves, L. C., Masiero, A. A., & de Castro, M. C. F. (2016). Application of Myo Armband System to Control a Robot Interface. Paper presented at the BIOSIGNALS.
17. Murillo, P. U., & Moreno, R. J. (2016). Individual robotic arms manipulator control employing electromyographic signals acquired by myo armbands. Int. J. Appl. Eng. Res, 11(23), 11241-11249.
18. North. (20.12.2019). from https://support.getmyo.com/hc/en-us
19. Said, S., Sheikh, M., Al-Rashidi, F., Lakys, Y., Beyrouthy, T., & Nait-ali, A. (2019). A Customizable Wearable Robust 3D Printed Bionic Arm: Muscle Controlled. Paper presented at the 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART).
20. Sathiyanarayanan, M., & Rajan, S. (2016). MYO Armband for physiotherapy healthcare: A case study using gesture recognition application. Paper presented at the 2016 8th International Conference on Communication Systems and Networks (COMSNETS).
21. Tabor, A., Kienzle, A., Smith, C., Watson, A., Wuertz, J., & Hanna, D. (2016). The Falling of Momo: A Myo-Electric Controlled Game to Support Research in Prosthesis Training. Paper presented at the Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts.
22. Uyar, E., Şenli, K., & Mutlu, L. (2012). Beyin Dalgası Kontrollü Protez Kol Tasarımı. Sakarya University Journal of Science, 16(3), 164-169.
23. Uzunhisarcıklı, E., Çetinkaya, M. B., Fidan, U., & Çalıkuşu, İ. (2019). Investigation of EMG Signals in Lower Extremity Muscle Groups During Robotic Gait Exercises. Avrupa Bilim ve Teknoloji Dergisi, 109-118.
24. Ülkir, O., Akgün, G., & Kaplanoğlu, E. (2017). Real time robotic arm control using wearable gesture armband. Paper presented at the Proc. 2nd International Mediterranean Science and Engineering Congress (IMSEC 2017).
25. Wopereis, H. W., Fumagalli, M., Stramigioli, S., & Carloni, R. (2015). Bilateral human-robot control for semi-autonomous UAV navigation. Paper presented at the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
26. Xu, Y., Yang, C., Liu, X., & Li, Z. (2018). A Teleoperated Shared Control Scheme for Mobile Robot Based sEMG. Paper presented at the 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM).
27. Yüzgeç, U., Büyüktepe, H. E., & Karakuzu, C. (2016). Kablosuz eldiven sistemi ile kontrol edilen robot kol tasarımı. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 9(2), 35-42.