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Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces

Year 2023, Volume: 26 Issue: 2, 973 - 981, 05.07.2023
https://doi.org/10.2339/politeknik.1117947

Abstract

In this study, a wearable electromyogram (EMG) system on the forearm was designed to analyze finger movements for use in human-machine interfaces. The designed system measures the EMG signals without restricting the user's movements, analyzes these measurements through the software embedded in the system, and transmits the generated response to the output units to be controlled in real-time with wireless communication techniques. In the study, a three-channel EMG amplifier was designed and a system in which the NodeMCU V3 development board could be integrated was realized.With the system, the features of finger movements were obtained using the Mean Absolute Value (MAV) and classified using Support Vector Machines (SVM) and Random Forest (RF) methods. In offline tests, 99.47% accursacy with RF and 98.2% accuracy with SVM were obtained. The RF algorithm with 99.47% accuracy in offline tests was selected and integrated into the embedded system for online tests. In the online tests performed with five volunteers, the system was able to analyze finger movements with an average accuracy of 92.16%, and the commands associated with the finger movements analyzed by the system were sent to the clients with the User Datagram Protocol (UDP), and the related movements were displayed on the output unit interface. The system can work in real-time with a delay of 90 ms and instantaneous movements can be seen visually on the designed output unit interface. This study is an important step in the detection of muscle diseases, the control of EMG-based wearable prosthetic systems, and the design of unmanned vehicles that can be controlled by finger movements.

Supporting Institution

TUBITAK 2209A

Project Number

Application Number: 1919B012001315

Thanks

This study was supported within the scope of TUBITAK 2209A (Application Number: 1919B012001315).

References

  • [1] Engin K. O. Ç., Bayat O., Duru D. G. and Duru A. D., “Göz hareketlerine dayalı beyin bilgisayar arayüzü tasarımı”, International Journal of Engineering Research and Development, 12(1): 176-188, (2020).
  • [2] Meyns P., Van de Crommert H. W. A. A., Rijken H., Van Kuppevelt D. H. J. M. and Duysens, J. "Locomotor training with body weight support in SCI: EMG improvement is more optimally expressed at a low testing speed” Spinal cord, 52(12): 887-893, (2014).
  • [3] Fong E. M. and Chung W. Y., “Mobile cloud-computing-based healthcare service by noncontact ECG monitoring” Sensors, 13(12): 16451-16473, (2013).
  • [4] Ocal H., DOĞRU İ. and BARIŞÇI N, “Internet of Things in Smart and Conventional Wearable Healthcare Devices”, JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 22(3), (2020).
  • [5] Aydin E. A., Bay O. F., & Guler I., “P300-based asynchronous brain computer interface for environmental control system”, IEEE journal of biomedical and health informatics, 22(3): 653-663, (2017).
  • [6] Uşaklı A. B., “Fizyolojik sinyallerin askerî amaçlı kullanılabilirliği: elektroensefalografi ve yakın kızılaltı spektroskopisi örnekleri”, Politeknik Dergisi, 21(4): 895-900, (2018).
  • [7] Kumar S., Dash A. and Mukul, M. K., “Design and development of low-cost eog acquisition circuit for hmi application”, In 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, 192-197, (2015).
  • [8] Ferreira A., Celeste W. C., Cheein F. A., Bastos-Filho T. F., Sarcinelli-Filho M. and Carelli R., “Human-machine interfaces based on EMG and EEG applied to robotic systems”, Journal of NeuroEngineering and Rehabilitation, 5(1): 1-15, (2008).
  • [9] Beck T. W. and Housh T. J., “Use of electromyography in studying human movement”, Routledge, New York, NY, USA, (2008).
  • [10] TAŞAR B., “EMG sinyalleri ile çok fonksiyonlu protez el simülatörünün kontrolü/Control of the multifunctional prosthetic hand simulator via EMG signals”, PhD Thesis, Firat University, 2016.
  • [11] Khan M. U., Aziz S., Sohail M., Shahid A. A. and Samer S., “Automated Detection and Classification of Gastrointestinal Diseases using surface-EMG Signals”, In 2019 22nd International Multitopic Conference (INMIC), IEEE, 1-8, (2019).
  • [12] Vaca Benitez L. M., Tabie M., Will N., Schmidt S., Jordan M. and Kirchner E. A., “Exoskeleton technology in rehabilitation: Towards an EMG-based orthosis system for upper limb neuromotor rehabilitation”, Journal of Robotics, (2013).
  • [13] Liu L., Liu P., Clancy E. A., Scheme E. and Englehart K. B., “Electromyogram whitening for improved classification accuracy in upper limb prosthesis control”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(5): 767-774, (2013).
  • [14] Assad C., Wolf M., Stoica A., Theodoridis T. and Glette K., “BioSleeve: A natural EMG-based interface for HRI”, In 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), IEEE, 69-70, (2013).
  • [15] Al-Jumaily A. and Olivares R. A., “Electromyogram (EMG) driven system based virtual reality for prosthetic and rehabilitation devices”, In Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services, 582-586, (2009).
  • [16] Zhang Z., Yang K., Qian J. and Zhang L., “Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network”, Sensors, 19(14): 3170, (2019).
  • [17] Chandrasekhar V., Vazhayil V. and Rao M., “Design of a real time portable low-cost multi-channel surface electromyography system to aid neuromuscular disorder and post stroke rehabilitation patients”, In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), sIEEE, 4138-4142, (2020).
  • [18] Antfolk C., Cipriani C., Controzzi M., Carrozza M. C., Lundborg G., Rosén B. and Sebelius F., “Using EMG for real-time prediction of joint angles to control a prosthetic hand equipped with a sensory feedback system” Journal of Medical and Biological Engineering, 30(6): 399-406, (2010).
  • [19] Malešević N., Marković D., Kanitz G., Controzzi M., Cipriani C. and Antfolk C., “Vector autoregressive hierarchical hidden Markov models for extracting finger movements using multichannel surface EMG signals” Complexity, (2018).
  • [20] Yamanoi Y., Ogiri Y., & Kato R., “EMG-based posture classification using a convolutional neural network for a myoelectric hand” Biomedical Signal Processing and Control, 55: 101574, (2020).
  • [21] Mayetin U., Küçük S. and Şayli̇ Ö., “EMG controlled mobile robot application” In 2015 Medical Technologies National Conference (TIPTEKNO), IEEE, 1-4, (2015).
  • [22] Onay F. and Mert A., “ Phasor represented EMG feature extraction against varying contraction level of prosthetic control”, Biomedical Signal Processing and Control, 59: 101881, (2020).
  • [23] Ariyanto M., Caesarendra W., Mustaqim K. A., Irfan M., Pakpahan J. A., Setiawan J. D. and Winoto A. R., “Finger movement pattern recognition method using artificial neural network based on electromyography (EMG) sensor” In 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), IEEE, 12-17, (2015).
  • [24] Caesarendra W., Tjahjowidodo T., Nico Y., Wahyudati S. and Nurhasanah L., “EMG finger movement classification based on ANFIS”, In Journal of Physics: 3conference series, 1007(1): 012005, (2018).
  • [25] Bonato P. (2005), “Advances in wearable technology and applications in physical medicine and rehabilitation”, Journal of neuroengineering and rehabilitation, 2(1): 1-4, (2005).
  • [26] Liu X., Sacks J., Zhang M., Richardson A. G., Lucas T. H. and Van der Spiegel J., “The virtual trackpad: An electromyography-based, wireless, real-time, low-power, embedded hand-gesture-recognition system using an event-driven artificial neural network”, IEEE Transactions on Circuits and Systems II: Express Briefs, 64(11): 1257-1261, (2016).
  • [27] Benatti S., Casamassima F., Milosevic B., Farella E., Schönle P., Fateh S., Burger T., Huang Q. and Benini L., “A versatile embedded platform for EMG acquisition and gesture recognition”, IEEE transactions on biomedical circuits and systems, 9(5): 620-630, (2015).
  • [28]Https://Espressif.Com/Sites/Default/Files/Documentation/0a-Esp8266ex_Datasheet_En.Pdf
  • [29] https://pypi.org/project/micromlgen/, “micromlgen 1.1.23”, (2021).
  • [30] Protocol U. D., "Rfc 768 j. postel isi 28 august 1980.", Isi, (1980).
  • [31] Duda R. O., Hart P. E. and Stork D. G., “Pattern Classification”, Wiley, USA 49-53 2012
  • [32] Baranauskas J., Oshiro T. and Perez, P. (2012), “How Many Trees in a Random Forest. Machiene learning”, Machine Learning and Data Mining in Pattern Recognition, 154-168, (2012).
  • [33] Leonardis D., Barsotti M., Loconsole C., Solazzi M., Troncossi M., Mazzotti C., Castelli V. P., Procopio C., Lamola G., Chisari C., Bergamasco M. and Frisoli A., “An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation”, IEEE transactions on haptics, 8(2): 140–151, (2015).
  • [34] Sadikoglu F., Kavalcioglu C. and Dagman B., “Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease”, Procedia computer science, 120: 422-429, (2017).
  • [35] Hardalaç F. , Poyraz M., “Yapay Sinir Ağları Kullanılarak EMG Sinyallerinin Sınıflandırılması ve Neuropathy Kas Hastalığının Teşhisi”, Politeknik Dergisi, 2002; 5(1): 75-83.
  • [36] Ali M., Riaz A., Usmani W. U. and Naseer N., “EMG Based Control of a Quadcopter”, In 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), IEEE, 250-254, (2020).

Parmak Hareketlerine Dayalı Gerçek Zamanlı İnsan-Makine Arayüzleri için Giyilebilir Elektromiyogram Tasarımı

Year 2023, Volume: 26 Issue: 2, 973 - 981, 05.07.2023
https://doi.org/10.2339/politeknik.1117947

Abstract

Bu çalışmada, insan-makine arayüzlerinde kullanılmak amacıyla, parmak hareketlerinin çözümlenebilmesine yönelik önkol üzerine giyilebilir bir elektromiyogram (EMG) sistemi tasarlanmıştır. Tasarlanan sistem, kullanıcının hareketlerini kısıtlamadan EMG sinyallerinin ölçümünü yaparak bu ölçümleri sisteme gömülü yazılım aracılığıyla çözümlemekte, oluşturulan cevabı, kontrol edilecek çıkış birimlerine kablosuz iletişim teknikleri ile gerçek zamanlı olarak iletebilmektedir. Çalışmada, üç kanal yapısındaki EMG yükseltecin tasarımı yapılmış ve NodeMCU V3 geliştirme kartının entegre edilebileceği bir sistem gerçekleştirilmiştir. Tasarlanan sistem ile parmak hareketlerine ait öznitelikler mutlak ortalama değer (MOD) kullanılarak elde edilmiş; Destek Vektör Makineleri (DVM) ve Rastgele Orman (RO) yöntemleri kullanılarak sınıflandırılmıştır. Offline testlerde, RO ile %99.47, DVM ile %98.2 doğruluk oranları elde edilmiştir. Offline testlerde %99.47 doğruluk gösteren RO algoritması seçilerek, online testler için gömülü sisteme entegre edilmiştir. Sistem 5 gönüllü ile gerçekleştirilen online testlerde parmak hareketlerini ortalama %92.16 doğrulukla çözümleyebilmiş, sistemin çözümlediği parmak hareketleri ile ilişkilendirilen komutların Kullanıcı Veribloğu İletişim Kuralları (UDP) ağ protokolü ile istemcilere gönderilerek ilgili hareketlerin çıkış birimi arayüzünde görüntülenmesi sağlanmıştır. Sistem 90 ms sürelik bir gecikme ile gerçek zamanlı olarak çalışabilmekte ve tasarlanan çıkış birimi arayüzünde anlık olarak yapılan hareketler görsel olarak görülebilmektedir. Yapılan bu çalışma kas hastalıklarının tespiti, EMG tabanlı giyilebilir protez sistemlerin kontrolü, parmak hareketleri ile kontrol edilebilecek insansız araçların tasarımında önemli bir aşamadır.

Project Number

Application Number: 1919B012001315

References

  • [1] Engin K. O. Ç., Bayat O., Duru D. G. and Duru A. D., “Göz hareketlerine dayalı beyin bilgisayar arayüzü tasarımı”, International Journal of Engineering Research and Development, 12(1): 176-188, (2020).
  • [2] Meyns P., Van de Crommert H. W. A. A., Rijken H., Van Kuppevelt D. H. J. M. and Duysens, J. "Locomotor training with body weight support in SCI: EMG improvement is more optimally expressed at a low testing speed” Spinal cord, 52(12): 887-893, (2014).
  • [3] Fong E. M. and Chung W. Y., “Mobile cloud-computing-based healthcare service by noncontact ECG monitoring” Sensors, 13(12): 16451-16473, (2013).
  • [4] Ocal H., DOĞRU İ. and BARIŞÇI N, “Internet of Things in Smart and Conventional Wearable Healthcare Devices”, JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI, 22(3), (2020).
  • [5] Aydin E. A., Bay O. F., & Guler I., “P300-based asynchronous brain computer interface for environmental control system”, IEEE journal of biomedical and health informatics, 22(3): 653-663, (2017).
  • [6] Uşaklı A. B., “Fizyolojik sinyallerin askerî amaçlı kullanılabilirliği: elektroensefalografi ve yakın kızılaltı spektroskopisi örnekleri”, Politeknik Dergisi, 21(4): 895-900, (2018).
  • [7] Kumar S., Dash A. and Mukul, M. K., “Design and development of low-cost eog acquisition circuit for hmi application”, In 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, 192-197, (2015).
  • [8] Ferreira A., Celeste W. C., Cheein F. A., Bastos-Filho T. F., Sarcinelli-Filho M. and Carelli R., “Human-machine interfaces based on EMG and EEG applied to robotic systems”, Journal of NeuroEngineering and Rehabilitation, 5(1): 1-15, (2008).
  • [9] Beck T. W. and Housh T. J., “Use of electromyography in studying human movement”, Routledge, New York, NY, USA, (2008).
  • [10] TAŞAR B., “EMG sinyalleri ile çok fonksiyonlu protez el simülatörünün kontrolü/Control of the multifunctional prosthetic hand simulator via EMG signals”, PhD Thesis, Firat University, 2016.
  • [11] Khan M. U., Aziz S., Sohail M., Shahid A. A. and Samer S., “Automated Detection and Classification of Gastrointestinal Diseases using surface-EMG Signals”, In 2019 22nd International Multitopic Conference (INMIC), IEEE, 1-8, (2019).
  • [12] Vaca Benitez L. M., Tabie M., Will N., Schmidt S., Jordan M. and Kirchner E. A., “Exoskeleton technology in rehabilitation: Towards an EMG-based orthosis system for upper limb neuromotor rehabilitation”, Journal of Robotics, (2013).
  • [13] Liu L., Liu P., Clancy E. A., Scheme E. and Englehart K. B., “Electromyogram whitening for improved classification accuracy in upper limb prosthesis control”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(5): 767-774, (2013).
  • [14] Assad C., Wolf M., Stoica A., Theodoridis T. and Glette K., “BioSleeve: A natural EMG-based interface for HRI”, In 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), IEEE, 69-70, (2013).
  • [15] Al-Jumaily A. and Olivares R. A., “Electromyogram (EMG) driven system based virtual reality for prosthetic and rehabilitation devices”, In Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services, 582-586, (2009).
  • [16] Zhang Z., Yang K., Qian J. and Zhang L., “Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network”, Sensors, 19(14): 3170, (2019).
  • [17] Chandrasekhar V., Vazhayil V. and Rao M., “Design of a real time portable low-cost multi-channel surface electromyography system to aid neuromuscular disorder and post stroke rehabilitation patients”, In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), sIEEE, 4138-4142, (2020).
  • [18] Antfolk C., Cipriani C., Controzzi M., Carrozza M. C., Lundborg G., Rosén B. and Sebelius F., “Using EMG for real-time prediction of joint angles to control a prosthetic hand equipped with a sensory feedback system” Journal of Medical and Biological Engineering, 30(6): 399-406, (2010).
  • [19] Malešević N., Marković D., Kanitz G., Controzzi M., Cipriani C. and Antfolk C., “Vector autoregressive hierarchical hidden Markov models for extracting finger movements using multichannel surface EMG signals” Complexity, (2018).
  • [20] Yamanoi Y., Ogiri Y., & Kato R., “EMG-based posture classification using a convolutional neural network for a myoelectric hand” Biomedical Signal Processing and Control, 55: 101574, (2020).
  • [21] Mayetin U., Küçük S. and Şayli̇ Ö., “EMG controlled mobile robot application” In 2015 Medical Technologies National Conference (TIPTEKNO), IEEE, 1-4, (2015).
  • [22] Onay F. and Mert A., “ Phasor represented EMG feature extraction against varying contraction level of prosthetic control”, Biomedical Signal Processing and Control, 59: 101881, (2020).
  • [23] Ariyanto M., Caesarendra W., Mustaqim K. A., Irfan M., Pakpahan J. A., Setiawan J. D. and Winoto A. R., “Finger movement pattern recognition method using artificial neural network based on electromyography (EMG) sensor” In 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), IEEE, 12-17, (2015).
  • [24] Caesarendra W., Tjahjowidodo T., Nico Y., Wahyudati S. and Nurhasanah L., “EMG finger movement classification based on ANFIS”, In Journal of Physics: 3conference series, 1007(1): 012005, (2018).
  • [25] Bonato P. (2005), “Advances in wearable technology and applications in physical medicine and rehabilitation”, Journal of neuroengineering and rehabilitation, 2(1): 1-4, (2005).
  • [26] Liu X., Sacks J., Zhang M., Richardson A. G., Lucas T. H. and Van der Spiegel J., “The virtual trackpad: An electromyography-based, wireless, real-time, low-power, embedded hand-gesture-recognition system using an event-driven artificial neural network”, IEEE Transactions on Circuits and Systems II: Express Briefs, 64(11): 1257-1261, (2016).
  • [27] Benatti S., Casamassima F., Milosevic B., Farella E., Schönle P., Fateh S., Burger T., Huang Q. and Benini L., “A versatile embedded platform for EMG acquisition and gesture recognition”, IEEE transactions on biomedical circuits and systems, 9(5): 620-630, (2015).
  • [28]Https://Espressif.Com/Sites/Default/Files/Documentation/0a-Esp8266ex_Datasheet_En.Pdf
  • [29] https://pypi.org/project/micromlgen/, “micromlgen 1.1.23”, (2021).
  • [30] Protocol U. D., "Rfc 768 j. postel isi 28 august 1980.", Isi, (1980).
  • [31] Duda R. O., Hart P. E. and Stork D. G., “Pattern Classification”, Wiley, USA 49-53 2012
  • [32] Baranauskas J., Oshiro T. and Perez, P. (2012), “How Many Trees in a Random Forest. Machiene learning”, Machine Learning and Data Mining in Pattern Recognition, 154-168, (2012).
  • [33] Leonardis D., Barsotti M., Loconsole C., Solazzi M., Troncossi M., Mazzotti C., Castelli V. P., Procopio C., Lamola G., Chisari C., Bergamasco M. and Frisoli A., “An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation”, IEEE transactions on haptics, 8(2): 140–151, (2015).
  • [34] Sadikoglu F., Kavalcioglu C. and Dagman B., “Electromyogram (EMG) signal detection, classification of EMG signals and diagnosis of neuropathy muscle disease”, Procedia computer science, 120: 422-429, (2017).
  • [35] Hardalaç F. , Poyraz M., “Yapay Sinir Ağları Kullanılarak EMG Sinyallerinin Sınıflandırılması ve Neuropathy Kas Hastalığının Teşhisi”, Politeknik Dergisi, 2002; 5(1): 75-83.
  • [36] Ali M., Riaz A., Usmani W. U. and Naseer N., “EMG Based Control of a Quadcopter”, In 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), IEEE, 250-254, (2020).
There are 36 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

İsmail Aydoğan 0000-0003-3721-3088

Eda Akman Aydın 0000-0002-9887-3808

Project Number Application Number: 1919B012001315
Publication Date July 5, 2023
Submission Date May 23, 2022
Published in Issue Year 2023 Volume: 26 Issue: 2

Cite

APA Aydoğan, İ., & Akman Aydın, E. (2023). Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces. Politeknik Dergisi, 26(2), 973-981. https://doi.org/10.2339/politeknik.1117947
AMA Aydoğan İ, Akman Aydın E. Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces. Politeknik Dergisi. July 2023;26(2):973-981. doi:10.2339/politeknik.1117947
Chicago Aydoğan, İsmail, and Eda Akman Aydın. “Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces”. Politeknik Dergisi 26, no. 2 (July 2023): 973-81. https://doi.org/10.2339/politeknik.1117947.
EndNote Aydoğan İ, Akman Aydın E (July 1, 2023) Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces. Politeknik Dergisi 26 2 973–981.
IEEE İ. Aydoğan and E. Akman Aydın, “Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces”, Politeknik Dergisi, vol. 26, no. 2, pp. 973–981, 2023, doi: 10.2339/politeknik.1117947.
ISNAD Aydoğan, İsmail - Akman Aydın, Eda. “Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces”. Politeknik Dergisi 26/2 (July 2023), 973-981. https://doi.org/10.2339/politeknik.1117947.
JAMA Aydoğan İ, Akman Aydın E. Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces. Politeknik Dergisi. 2023;26:973–981.
MLA Aydoğan, İsmail and Eda Akman Aydın. “Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces”. Politeknik Dergisi, vol. 26, no. 2, 2023, pp. 973-81, doi:10.2339/politeknik.1117947.
Vancouver Aydoğan İ, Akman Aydın E. Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces. Politeknik Dergisi. 2023;26(2):973-81.