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Investigating the Impact of Finger Movements on Musical Interaction Performance Using an EMG-Based MYO Armband

Year 2025, Volume: 15 Issue: 2, 661 - 687, 15.06.2025

Eda Çetin , İlayda Kaya , Kübra Erat , Pınar Onay Durdu

https://doi.org/10.31466/kfbd.1553565

Abstract

Modern technologies such as wearable sensors and camera-based devices have enabled the development of a variety of gesture-based controllers that fall within the scope of New Interfaces for Musical Expression (NIMEs), enabling non-contact musical performances. This study focuses on the performance of the MYO armband in the context of NIMEs, specifically enabling users to play simple piano pieces through EMG signals without physical contact with the piano. EMG data was collected from the users' arm muscles during piano playing and machine learning algorithms (SVM - Support Vector Machine, GB - Gradient Boosting and RF - Random Forest) were used for classification. The results demonstrated that after preprocessing the EMG signals, the GB algorithm achieved the highest accuracy of 97.40% for classification. The findings of the study suggest that EMG-based technologies such as the MYO armband can enhance the interactive music-making experience and enable direct music performance.

Keywords

EMG MYO armband New interfaces for musical expression (NIME) Machine learning Natural interaction

Project Number

1919B012334786

References

  • Arozi, M., Ariyanto, M., Kristianto, A., and Setiawan, J. D. (2020). EMG signal processing of Myo armband sensor for prosthetic hand input using RMS and ANFIS. In 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 36-40), Semarang, Indonesia, doi:10.1109/ICITACEE50144.2020.9239169.
  • Atau, T., Musical technical issues in using interactive instrument technology with applications to the BioMuse. In Proceedings of 1993 International Computer Music Conference, Tokio, Japan, 124–126, September 10-15, pp. 124–126.
  • Aydoğan, İ., and Aydın, E. A. (2023). Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces. Politeknik Dergisi, 26(2), 973-981, doi:10.2339/politeknik.1117947.
  • Biau, G., and Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197-227, doi:10.48550/arXiv.1511.05741
  • Boostani, R., and Moradi, M. H. (2003). Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiological measurement, IOP Publishing, 24(2), 309, doi:10.1088/0967-3334/24/2/307.
  • Chalard, A., Belle, M., Montané, E., Marque, P., Amarantini, D., and Gasq, D. (2020). Impact of the EMG normalization method on muscle activation and the antagonist-agonist co-contraction index during active elbow extension: Practical implications for post-stroke subjects. Journal of Electromyography and Kinesiology, 51, 102403, doi:10.1016/j.jelekin.2020.102403.
  • Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794), San Francisco, California, USA, doi:10.48550/arXiv.1603.02754.
  • Chung, E. A., and Benalcázar, M. E. (2019). Real-time hand gesture recognition model using deep learning techniques and EMG signals. In 2019 27th European Signal Processing Conference (EUSIPCO) (pp. 1-5), A Coruna, Spain, doi:10.23919/EUSIPCO.2019.8903136.
  • Donnarumma, M., Caramiaux, B., and Tanaka, A. (2013). Muscular Interactions Combining EMG and MMG sensing for musical practice. In: Proceedings of the International Conference on New Interfaces for Musical Expression (pp. 1–4) Dajeon, Seoul, Korea, doi:10.5281/zenodo.1178504.
  • Düzenli, M., Salur, K., Erat, K., and Durdu, P. O. (2023). Turkish Sign Language Recognition by Using Wearable MYO Armband. In: García Márquez, F.P., Jamil, A., Eken, S., Hameed, A.A. (eds), International Conference on Computing, Intelligence and Data Analytics ICCIDA 2022 (pp. 344–357). Lecture Notes in Networks and Systems (Cham: Springer International Publishing), 643, Kocaeli, Turkey, doi:10.1007/978-3-031-27099-4_27.
  • Erdem, C., Lan, Q., and Jensenius, A. R. (2020). Exploring relationships between effort, motion, and sound in new musical instruments. Human Technology, 16(3), 310-347, doi: 10.17011/ht/urn.202011256767.
  • Fandaklı, S. A., and Okumuş, H. (2023). Comparison of artificial neural networks with other machine learning methods in foot movement classification. Karadeniz Fen Bilimleri Dergisi, 13(1), 153-171, doi:10.31466/kfbd.1214950.
  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics, 1189-1232, doi:10.1214/aos/1013203451.
  • Jensenius A. R., Gonzalez Sanchez V. E., Zelechowska, A., and Bjerkestrand, K. A. V. (2017). Exploring the Myo controller for sonic microinteraction, In Proceedings of the International Conference on New Interfaces for Musical Expression (pp. 442-445), Copenhagen, Denmark, doi: 10.5281/zenodo.1176308.
  • Kamen, G., and Kinesiology, E. (2004). Research methods in biomechanics. Human Kinetics Publ: Champaign, IL, USA.
  • Kandemir, G. (2013). Design and implementation of a device to control a robotic arm by EMG signal. Yüksek Lisans Tezi. Middle East Technical University, Ankara, Türkiye.
  • Karjalainen, M., Mäki-Patola, T., Kanerva, A., & Huovilainen, A. (2006). Virtual air guitar. Journal of the Audio Engineering Society, 54(10), 964-980.
  • Karolus, J., Schuff, H., Kosch, T., Wozniak, P. W., and Schmidt, A. (2018). EMGuitar: Assisting Guitar Playing with Electromyography. In Proceedings of the 2018 Designing Interactive Systems Conference (DIS ’18) (pp. 651–655). Association for Computing Machinery, New York, NY, USA, doi:10.1145/3196709.3196803.
  • Khushaba, R. N., Kodagoda, S., Takruri, M., & Dissanayake, G. (2012). Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Systems with Applications, 39(12), 10731-10738, doi:10.1016/j.eswa.2012.02.192.
  • Kilian, A., Karolus, J., Kosch, T., Schmidt, A., and Woźniak, P. W. (2021). EMPiano: Electromyographic Pitch Control on the Piano Keyboard. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp.1-4), Yokohama, Japan, doi: 10.1145/3411763.3451556.
  • Knapp, R. B., & Lusted, H. S. (1990). A bioelectric controller for computer music applications. Computer music journal, 14(1), 42-47, doi:10.2307/3680115.
  • Lee D. E., (2017). Electromyogram-based Interface for Musical Performance. Yüksek Lisans Tezi, Ewha Womans University, Güney Kore.
  • Mani, S., and Rao, M. (2021). Feasibility study of using myoband for learning electronic keyboard. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 10196-10202), Milan, Italy, doi: 10.1109/ICPR48806.2021.9412954.
  • Miranda, E. R., and Wanderley, M. M. (2006). New digital musical instruments: control and interaction beyond the keyboard, Vol. 21. AR Editions, Inc.
  • Natekin, A., and Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7(21), doi:10.3389/fnbot.2013.00021.
  • Ni, S., Al-qaness, M. A., Hawbani, A., Al-Alimi, D., Abd Elaziz, M., and Ewees, A. A. (2024). A survey on hand gesture recognition based on surface electromyography: Fundamentals, methods, applications, challenges and future trends. Applied Soft Computing, 112235, doi:10.1016/j.asoc.2024.112235.
  • Nymoen, K., Haugen, M. R., and Jensenius, A. R. (2015). Mumyo–evaluating and exploring the myo armband for musical interaction. In NIME 2015: Proceedings of the international conference on New Interfaces for Musical Expression (pp. 215-218), Baton Rouge, LA, USA, doi: 10.5281/zenodo.1179150.
  • Pakarinen, J., Puputti, T., and Välimäki, V. (2008). Virtual slide guitar. Computer Music Journal, 32(3), 42-54. JSTOR, doi:10.1162/comj.2008.32.3.42.
  • Powers, D. M. W., (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. International Journal of Machine Learning Technology. 2:1 (2011), 37-63, doi:10.48550/arXiv.2010.16061.
  • Putro, N. A. S., Avian, C., Prakosa, S. W., Mahali, M. I., and Leu, J.-S. (2024). Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model. Biomedical Signal Processing and Control, 87, 105447, doi:10.1016/j.bspc.2023.105447.
  • Rahman, K. M., and Nasor, K. M. (2024). Classification of Finger Movements Using Multi-channel EMG and Machine Learning. In: Kumar Jain, P., Nath Singh, Y., Gollapalli, R.P., Singh, S.P. (eds) Advances in Signal Processing and Communication Engineering (ICASPACE 2023) (pp. 439-451), Lecture Notes in Electrical Engineering, vol 1157. Springer, Singapore, doi:10.1007/978-981-97-0562-7_33.
  • Reaz, M. B. I., Hussain, M. S., and Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: Detection, processing, classification and applications. Biological Procedures Online, 8(1), 11-35, doi:10.1251/bpo115.
  • Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine, 47(1), 31-39, doi:10.17849/insm-47-01-31-39.1.
  • Sapsanis, C. (2013). Recognition of basic hand movements using electromyography. Yüksek Lisans Tezi. University of Patras, Greece.
  • Sapsanis, C., Georgoulas, G., Tzes, A., and Lymberopoulos, D. (2013). Improving EMG based classification of basic hand movements using EMD. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5754-5757), Osaka, Japan, doi:10.1109/EMBC.2013.6610858.
  • Tamani, J. E., Cruz, J. C. B., Cruzada, J. R., Valenzuela, J., Chan, K. G., and Deja, J. A. (2018). Building Guitar Strum Models for an Interactive Air Guitar Prototype. In Proceedings of the 4th International Conference on Human-Computer Interaction and User Experience in Indonesia, CHIuXiD ’18 (pp. 18-22), Yogyakarta, Indonesia, doi: 10.1145/3205946.3205972.
  • Tanaka, A., and Knapp, R. B. (2002). Multimodal interaction in music using the electromyogram and relative position sensing. Paper presented at International Conference on New Interfaces for Musical Expression (NIME), Dublin, Ireland.
  • Tanaka, A., ve Ortiz, M. (2017). Gestural musical performance with physiological sensors, focusing on the electromyogram. In The Routledge companion to embodied music interaction (pp. 420-428). 1st Edition, Routledge, doi: 10.4324/9781315621364-46.
  • Tanaka, A., Sèdes, A., Bonardi, A., Whitmarsh, S., Fierro, D., and Di Maggio, F. (2023). Brain-Body Digital Musical Instrument Work-in-Progress. In ISEA 2023 - 28th International Symposium on Electronic Art, Le Cube Garges; Ecole Nationale Supérieure des Arts Décoratifs; Forum des Images, Paris, France, doi:hal-04259596.
  • Tepe, C., and Erdim, M. (2022). Classification of surface electromyography and gyroscopic signals of finger gestures acquired by Myo armband using machine learning methods. Biomedical Signal Processing and Control, 75, 103588, doi:10.1016/j.bspc.2022.103588.
  • URL-1: https://www.bbc.com/news/science-environment-18196349 (Erişim Tarihi: 14 Ağustos 2024).
  • URL-2: https://teachablemachine.withgoogle.com/ (Erişim Tarihi: 14 Ağustos 2024).
  • URL-3: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html (Erişim Tarihi: 10 Şubat 2025).
  • Zhang, Z., Yang, K., Qian, J., & Zhang, L. (2019). Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network. Sensors, 19(14), 3170, doi:10.3390/s19143170.

EMG Tabanlı MYO Kol Bandı ile Parmak Hareketlerinin Müzikal Etkileşim Performansına Etkisinin Araştırılması

Year 2025, Volume: 15 Issue: 2, 661 - 687, 15.06.2025

Eda Çetin , İlayda Kaya , Kübra Erat , Pınar Onay Durdu

https://doi.org/10.31466/kfbd.1553565

Abstract

Giyilebilir sensörler ve kamera tabanlı cihazlar gibi modern teknolojiler, temassız müzik performanslarına olanak tanıyan Müzikal İfade için Yeni Arayüzler (MİYA) kapsamında olan çeşitli jest tabanlı kontrolörlerin geliştirilmesini sağlamıştır. Bu çalışma, MYO kol bandının MİYA bağlamındaki performansına odaklanmakta, özellikle kullanıcıların piyano ile fiziksel temas olmadan EMG sinyalleri aracılığıyla basit piyano parçaları çalmalarını sağlamaktadır. Piyano çalma sırasında kullanıcıların kol kaslarından EMG verileri toplanmış ve sınıflandırma için makine öğrenimi algoritmaları (SVM - Destek Vektör Makineleri, GB - Gradyan Güçlendirme ve RF - Rastgele Orman) kullanılmıştır. Sonuçlar, EMG sinyallerinin ön işleme sonrası GB algoritmasının %97,40 ile en yüksek doğruluğu elde ettiğini göstermiştir. Çalışmada elde edilen bulgular, MYO kol bandı gibi EMG tabanlı teknolojilerin etkileşimli müzik yapma deneyimini geliştirebileceğini ve doğrudan müzik performansına olanak sağlayabileceğini göstermektedir.

Keywords

EMG MYO kol bandı Müzikal ifade için yeni arayüzler (MİYA) Makine öğrenmesi Doğal etkileşim

Ethical Statement

Bu makalenin yazarları çalışmalarında kullandıkları materyal ve yöntemlerin etik kurul izni ve/veya yasal-özel bir izin gerektirmediğini beyan ederler.

Supporting Institution

2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı

Project Number

1919B012334786

Thanks

1919B012334786 proje nolu bu çalışma 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında desteklenmiştir.

References

  • Arozi, M., Ariyanto, M., Kristianto, A., and Setiawan, J. D. (2020). EMG signal processing of Myo armband sensor for prosthetic hand input using RMS and ANFIS. In 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 36-40), Semarang, Indonesia, doi:10.1109/ICITACEE50144.2020.9239169.
  • Atau, T., Musical technical issues in using interactive instrument technology with applications to the BioMuse. In Proceedings of 1993 International Computer Music Conference, Tokio, Japan, 124–126, September 10-15, pp. 124–126.
  • Aydoğan, İ., and Aydın, E. A. (2023). Wearable Electromyogram Design for Finger Movements Based Real-Time Human-Machine Interfaces. Politeknik Dergisi, 26(2), 973-981, doi:10.2339/politeknik.1117947.
  • Biau, G., and Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197-227, doi:10.48550/arXiv.1511.05741
  • Boostani, R., and Moradi, M. H. (2003). Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiological measurement, IOP Publishing, 24(2), 309, doi:10.1088/0967-3334/24/2/307.
  • Chalard, A., Belle, M., Montané, E., Marque, P., Amarantini, D., and Gasq, D. (2020). Impact of the EMG normalization method on muscle activation and the antagonist-agonist co-contraction index during active elbow extension: Practical implications for post-stroke subjects. Journal of Electromyography and Kinesiology, 51, 102403, doi:10.1016/j.jelekin.2020.102403.
  • Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794), San Francisco, California, USA, doi:10.48550/arXiv.1603.02754.
  • Chung, E. A., and Benalcázar, M. E. (2019). Real-time hand gesture recognition model using deep learning techniques and EMG signals. In 2019 27th European Signal Processing Conference (EUSIPCO) (pp. 1-5), A Coruna, Spain, doi:10.23919/EUSIPCO.2019.8903136.
  • Donnarumma, M., Caramiaux, B., and Tanaka, A. (2013). Muscular Interactions Combining EMG and MMG sensing for musical practice. In: Proceedings of the International Conference on New Interfaces for Musical Expression (pp. 1–4) Dajeon, Seoul, Korea, doi:10.5281/zenodo.1178504.
  • Düzenli, M., Salur, K., Erat, K., and Durdu, P. O. (2023). Turkish Sign Language Recognition by Using Wearable MYO Armband. In: García Márquez, F.P., Jamil, A., Eken, S., Hameed, A.A. (eds), International Conference on Computing, Intelligence and Data Analytics ICCIDA 2022 (pp. 344–357). Lecture Notes in Networks and Systems (Cham: Springer International Publishing), 643, Kocaeli, Turkey, doi:10.1007/978-3-031-27099-4_27.
  • Erdem, C., Lan, Q., and Jensenius, A. R. (2020). Exploring relationships between effort, motion, and sound in new musical instruments. Human Technology, 16(3), 310-347, doi: 10.17011/ht/urn.202011256767.
  • Fandaklı, S. A., and Okumuş, H. (2023). Comparison of artificial neural networks with other machine learning methods in foot movement classification. Karadeniz Fen Bilimleri Dergisi, 13(1), 153-171, doi:10.31466/kfbd.1214950.
  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics, 1189-1232, doi:10.1214/aos/1013203451.
  • Jensenius A. R., Gonzalez Sanchez V. E., Zelechowska, A., and Bjerkestrand, K. A. V. (2017). Exploring the Myo controller for sonic microinteraction, In Proceedings of the International Conference on New Interfaces for Musical Expression (pp. 442-445), Copenhagen, Denmark, doi: 10.5281/zenodo.1176308.
  • Kamen, G., and Kinesiology, E. (2004). Research methods in biomechanics. Human Kinetics Publ: Champaign, IL, USA.
  • Kandemir, G. (2013). Design and implementation of a device to control a robotic arm by EMG signal. Yüksek Lisans Tezi. Middle East Technical University, Ankara, Türkiye.
  • Karjalainen, M., Mäki-Patola, T., Kanerva, A., & Huovilainen, A. (2006). Virtual air guitar. Journal of the Audio Engineering Society, 54(10), 964-980.
  • Karolus, J., Schuff, H., Kosch, T., Wozniak, P. W., and Schmidt, A. (2018). EMGuitar: Assisting Guitar Playing with Electromyography. In Proceedings of the 2018 Designing Interactive Systems Conference (DIS ’18) (pp. 651–655). Association for Computing Machinery, New York, NY, USA, doi:10.1145/3196709.3196803.
  • Khushaba, R. N., Kodagoda, S., Takruri, M., & Dissanayake, G. (2012). Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Systems with Applications, 39(12), 10731-10738, doi:10.1016/j.eswa.2012.02.192.
  • Kilian, A., Karolus, J., Kosch, T., Schmidt, A., and Woźniak, P. W. (2021). EMPiano: Electromyographic Pitch Control on the Piano Keyboard. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp.1-4), Yokohama, Japan, doi: 10.1145/3411763.3451556.
  • Knapp, R. B., & Lusted, H. S. (1990). A bioelectric controller for computer music applications. Computer music journal, 14(1), 42-47, doi:10.2307/3680115.
  • Lee D. E., (2017). Electromyogram-based Interface for Musical Performance. Yüksek Lisans Tezi, Ewha Womans University, Güney Kore.
  • Mani, S., and Rao, M. (2021). Feasibility study of using myoband for learning electronic keyboard. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 10196-10202), Milan, Italy, doi: 10.1109/ICPR48806.2021.9412954.
  • Miranda, E. R., and Wanderley, M. M. (2006). New digital musical instruments: control and interaction beyond the keyboard, Vol. 21. AR Editions, Inc.
  • Natekin, A., and Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7(21), doi:10.3389/fnbot.2013.00021.
  • Ni, S., Al-qaness, M. A., Hawbani, A., Al-Alimi, D., Abd Elaziz, M., and Ewees, A. A. (2024). A survey on hand gesture recognition based on surface electromyography: Fundamentals, methods, applications, challenges and future trends. Applied Soft Computing, 112235, doi:10.1016/j.asoc.2024.112235.
  • Nymoen, K., Haugen, M. R., and Jensenius, A. R. (2015). Mumyo–evaluating and exploring the myo armband for musical interaction. In NIME 2015: Proceedings of the international conference on New Interfaces for Musical Expression (pp. 215-218), Baton Rouge, LA, USA, doi: 10.5281/zenodo.1179150.
  • Pakarinen, J., Puputti, T., and Välimäki, V. (2008). Virtual slide guitar. Computer Music Journal, 32(3), 42-54. JSTOR, doi:10.1162/comj.2008.32.3.42.
  • Powers, D. M. W., (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. International Journal of Machine Learning Technology. 2:1 (2011), 37-63, doi:10.48550/arXiv.2010.16061.
  • Putro, N. A. S., Avian, C., Prakosa, S. W., Mahali, M. I., and Leu, J.-S. (2024). Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model. Biomedical Signal Processing and Control, 87, 105447, doi:10.1016/j.bspc.2023.105447.
  • Rahman, K. M., and Nasor, K. M. (2024). Classification of Finger Movements Using Multi-channel EMG and Machine Learning. In: Kumar Jain, P., Nath Singh, Y., Gollapalli, R.P., Singh, S.P. (eds) Advances in Signal Processing and Communication Engineering (ICASPACE 2023) (pp. 439-451), Lecture Notes in Electrical Engineering, vol 1157. Springer, Singapore, doi:10.1007/978-981-97-0562-7_33.
  • Reaz, M. B. I., Hussain, M. S., and Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: Detection, processing, classification and applications. Biological Procedures Online, 8(1), 11-35, doi:10.1251/bpo115.
  • Rigatti, S. J. (2017). Random forest. Journal of Insurance Medicine, 47(1), 31-39, doi:10.17849/insm-47-01-31-39.1.
  • Sapsanis, C. (2013). Recognition of basic hand movements using electromyography. Yüksek Lisans Tezi. University of Patras, Greece.
  • Sapsanis, C., Georgoulas, G., Tzes, A., and Lymberopoulos, D. (2013). Improving EMG based classification of basic hand movements using EMD. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5754-5757), Osaka, Japan, doi:10.1109/EMBC.2013.6610858.
  • Tamani, J. E., Cruz, J. C. B., Cruzada, J. R., Valenzuela, J., Chan, K. G., and Deja, J. A. (2018). Building Guitar Strum Models for an Interactive Air Guitar Prototype. In Proceedings of the 4th International Conference on Human-Computer Interaction and User Experience in Indonesia, CHIuXiD ’18 (pp. 18-22), Yogyakarta, Indonesia, doi: 10.1145/3205946.3205972.
  • Tanaka, A., and Knapp, R. B. (2002). Multimodal interaction in music using the electromyogram and relative position sensing. Paper presented at International Conference on New Interfaces for Musical Expression (NIME), Dublin, Ireland.
  • Tanaka, A., ve Ortiz, M. (2017). Gestural musical performance with physiological sensors, focusing on the electromyogram. In The Routledge companion to embodied music interaction (pp. 420-428). 1st Edition, Routledge, doi: 10.4324/9781315621364-46.
  • Tanaka, A., Sèdes, A., Bonardi, A., Whitmarsh, S., Fierro, D., and Di Maggio, F. (2023). Brain-Body Digital Musical Instrument Work-in-Progress. In ISEA 2023 - 28th International Symposium on Electronic Art, Le Cube Garges; Ecole Nationale Supérieure des Arts Décoratifs; Forum des Images, Paris, France, doi:hal-04259596.
  • Tepe, C., and Erdim, M. (2022). Classification of surface electromyography and gyroscopic signals of finger gestures acquired by Myo armband using machine learning methods. Biomedical Signal Processing and Control, 75, 103588, doi:10.1016/j.bspc.2022.103588.
  • URL-1: https://www.bbc.com/news/science-environment-18196349 (Erişim Tarihi: 14 Ağustos 2024).
  • URL-2: https://teachablemachine.withgoogle.com/ (Erişim Tarihi: 14 Ağustos 2024).
  • URL-3: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html (Erişim Tarihi: 10 Şubat 2025).
  • Zhang, Z., Yang, K., Qian, J., & Zhang, L. (2019). Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network. Sensors, 19(14), 3170, doi:10.3390/s19143170.
There are 44 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Articles
Authors

Eda Çetin 0009-0003-5434-9478

İlayda Kaya 0009-0003-0932-4116

Kübra Erat 0000-0001-8336-2979

Pınar Onay Durdu 0000-0002-0439-1702

Project Number 1919B012334786
Publication Date June 15, 2025
Submission Date September 20, 2024
Acceptance Date May 15, 2025
Published in Issue Year 2025 Volume: 15 Issue: 2

Cite

APA Çetin, E., Kaya, İ., Erat, K., Onay Durdu, P. (2025). EMG Tabanlı MYO Kol Bandı ile Parmak Hareketlerinin Müzikal Etkileşim Performansına Etkisinin Araştırılması. Karadeniz Fen Bilimleri Dergisi, 15(2), 661-687. https://doi.org/10.31466/kfbd.1553565
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