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Detection and Classification of Muscle Activation in EMG Data Acquired by Myo Armband

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 178 - 183, 15.08.2020
https://doi.org/10.31590/ejosat.779660

Öz

Electromyogram (EMG) signals are signals that contain information about contractions in the muscles. EMG signals are personal and express which muscles contract at what intensity. In detecting these signals, Myo armband has been used frequently in recent years. There are eight EMG sensors, accelerometer sensors and gyroscopes on the Myo armband. These eight EMG sensors settle on different muscles on the arm and measure the contraction intension of the muscles during gesture. In this way, the gesture using the information of which of the eight sensors is contracted can be recognized. Myo armband acquire EMG data with a sampling frequency of 200 Hz. In this study, EMG data was acquired by repeating 10 times 4 different hand gestures by 4 subject by attaching Myo armband to the right forearm. First, a high pass filter was applied to eliminate the noise from the acquired data and then the times when the hand gesture started and ended were determined. The aim of this study is to propose a new method to the literature to find the start and the end times of hand gesture at this point. Five time domain features of the preprocessed EMG signals were extracted. These features were root mean squire (RMS), mean absolute value (MAV), zero crossing (ZC), waveform length (WL) and slope sign change (SSC). Sequential forward selection was made in order to find the most successful feature set among the extracted features. For classification, SVM and KNN algorithms were used. As a result of the study, SVM algorithm with the WL feature gave the best result and 98.75% performance was achieved. The result obtained was compared with the studies in the literature. In addition, other methods in the literature used to find the times when the gesture starts and ends were applied to the dataset used in this study and the results were shown.

Kaynakça

  • Abduo, M., & Galster, M. (2015). Myo gesture control armband for medical applications.
  • Barioul, R., Fakhfakh, S., Derbel, H., & Kanoun, O. (2019). Evaluation of EMG Signal Time Domain Features for Hand Gesture Distinction. Paper presented at the 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD).
  • Benalcázar, M. E., Motoche, C., Zea, J. A., Jaramillo, A. G., Anchundia, C. E., Zambrano, P., . . . Pérez, M. (2017). Real-time hand gesture recognition using the myo armband and muscle activity detection. Paper presented at the 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM).
  • Chen, W., & Zhang, Z. (2019). Hand Gesture Recognition using sEMG Signals Based on Support Vector Machine. Paper presented at the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC).
  • 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* This work was partially supported by the Swiss National Science Foundation Sinergia project# 410160837 MeganePro. Paper presented at the 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob).
  • Cote-Allard, U., Fall, C. L., Campeau-Lecours, A., Gosselin, C., Laviolette, F., & Gosselin, B. (2017). Transfer learning for sEMG hand gestures recognition using convolutional neural networks. Paper presented at the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
  • De Luca, C. J., Gilmore, L. D., Kuznetsov, M., & Roy, S. H. (2010). Filtering the surface EMG signal: Movement artifact and baseline noise contamination. Journal of biomechanics, 43(8), 1573-1579.
  • 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.
  • İŞcan, M., Emeç, C., & YeŞİldİrek, A. (2018). Hand gesture movement classification based on dynamically structured neural network. Paper presented at the 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT).
  • Kocyigit, Y., & Kilic, I. (2008). Using LBG algorithm for extracting the features of EMG signals. Paper presented at the 2008 IEEE 16th Signal Processing, Communication and Applications Conference.
  • Kunapipat, M., Phukpattaranont, P., Neranon, P., & Thongpull, K. (2018). Sensor-assisted EMG data recording system. Paper presented at the 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).
  • 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.
  • Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert systems with applications, 39(8), 7420-7431.
  • Phinyomark, A., & Scheme, E. (2018). A feature extraction issue for myoelectric control based on wearable EMG sensors. Paper presented at the 2018 IEEE Sensors Applications Symposium (SAS).
  • Ploengpit, Y., & Phienthrakul, T. (2016). Rock-paper-scissors with Myo Armband pose detection. Paper presented at the 2016 International Computer Science and Engineering Conference (ICSEC).
  • Robertson, G. E., Caldwell, G. E., Hamill, J., Kamen, G., & Whittlesey, S. (2013). Research methods in biomechanics: Human kinetics.
  • Stańczyk, U., & Jain, L. C. (2015). Feature selection for data and pattern recognition: Springer.
  • 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.
  • Vachirapipop, M., Soymat, S., Tiraronnakul, W., & Hnoohom, N. (2017). An integration of Myo Armbands and an android-based mobile application for communication with hearing-impaired persons. Paper presented at the 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).
  • Wahid, M. F., Tafreshi, R., Al-Sowaidi, M., & Langari, R. (2018). Subject-independent hand gesture recognition using normalization and machine learning algorithms. Journal of computational science, 27, 69-76.
  • Wibawa, A. D., & Sumpeno, S. (2017). Gesture Recognition for Indonesian Sign Language Systems (ISLS) Using Multimodal Sensor Leap Motion and Myo Armband Controllers Based-on Naïve Bayes Classifier. Paper presented at the 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT).
  • Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9), 2839-2846.
  • Yang, J., Pan, J., & Li, J. (2017). sEMG-based continuous hand gesture recognition using GMM-HMM and threshold model. Paper presented at the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).

Detection and Classification of Muscle Activation in EMG Data Acquired by Myo Armband

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 178 - 183, 15.08.2020
https://doi.org/10.31590/ejosat.779660

Öz

Electromyogram (EMG) signals are signals that contain information about contractions in the muscles. EMG signals are personal and express which muscles contract at what intensity. In detecting these signals, Myo armband has been used frequently in recent years. There are eight EMG sensors, accelerometer sensors and gyroscopes on the Myo armband. These eight EMG sensors settle on different muscles on the arm and measure the contraction intension of the muscles during gesture. In this way, the gesture using the information of which of the eight sensors is contracted can be recognized. Myo armband acquire EMG data with a sampling frequency of 200 Hz. In this study, EMG data was acquired by repeating 10 times 4 different hand gestures by 4 subject by attaching Myo armband to the right forearm. First, a high pass filter was applied to eliminate the noise from the acquired data and then the times when the hand gesture started and ended were determined. The aim of this study is to propose a new method to the literature to find the start and the end times of hand gesture at this point. Five time domain features of the preprocessed EMG signals were extracted. These features were root mean squire (RMS), mean absolute value (MAV), zero crossing (ZC), waveform length (WL) and slope sign change (SSC). Sequential forward selection was made in order to find the most successful feature set among the extracted features. For classification, SVM and KNN algorithms were used. As a result of the study, SVM algorithm with the WL feature gave the best result and 98.75% performance was achieved. The result obtained was compared with the studies in the literature. In addition, other methods in the literature used to find the times when the gesture starts and ends were applied to the dataset used in this study and the results were shown.

Kaynakça

  • Abduo, M., & Galster, M. (2015). Myo gesture control armband for medical applications.
  • Barioul, R., Fakhfakh, S., Derbel, H., & Kanoun, O. (2019). Evaluation of EMG Signal Time Domain Features for Hand Gesture Distinction. Paper presented at the 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD).
  • Benalcázar, M. E., Motoche, C., Zea, J. A., Jaramillo, A. G., Anchundia, C. E., Zambrano, P., . . . Pérez, M. (2017). Real-time hand gesture recognition using the myo armband and muscle activity detection. Paper presented at the 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM).
  • Chen, W., & Zhang, Z. (2019). Hand Gesture Recognition using sEMG Signals Based on Support Vector Machine. Paper presented at the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC).
  • 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* This work was partially supported by the Swiss National Science Foundation Sinergia project# 410160837 MeganePro. Paper presented at the 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob).
  • Cote-Allard, U., Fall, C. L., Campeau-Lecours, A., Gosselin, C., Laviolette, F., & Gosselin, B. (2017). Transfer learning for sEMG hand gestures recognition using convolutional neural networks. Paper presented at the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
  • De Luca, C. J., Gilmore, L. D., Kuznetsov, M., & Roy, S. H. (2010). Filtering the surface EMG signal: Movement artifact and baseline noise contamination. Journal of biomechanics, 43(8), 1573-1579.
  • 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.
  • İŞcan, M., Emeç, C., & YeŞİldİrek, A. (2018). Hand gesture movement classification based on dynamically structured neural network. Paper presented at the 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT).
  • Kocyigit, Y., & Kilic, I. (2008). Using LBG algorithm for extracting the features of EMG signals. Paper presented at the 2008 IEEE 16th Signal Processing, Communication and Applications Conference.
  • Kunapipat, M., Phukpattaranont, P., Neranon, P., & Thongpull, K. (2018). Sensor-assisted EMG data recording system. Paper presented at the 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).
  • 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.
  • Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert systems with applications, 39(8), 7420-7431.
  • Phinyomark, A., & Scheme, E. (2018). A feature extraction issue for myoelectric control based on wearable EMG sensors. Paper presented at the 2018 IEEE Sensors Applications Symposium (SAS).
  • Ploengpit, Y., & Phienthrakul, T. (2016). Rock-paper-scissors with Myo Armband pose detection. Paper presented at the 2016 International Computer Science and Engineering Conference (ICSEC).
  • Robertson, G. E., Caldwell, G. E., Hamill, J., Kamen, G., & Whittlesey, S. (2013). Research methods in biomechanics: Human kinetics.
  • Stańczyk, U., & Jain, L. C. (2015). Feature selection for data and pattern recognition: Springer.
  • 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.
  • Vachirapipop, M., Soymat, S., Tiraronnakul, W., & Hnoohom, N. (2017). An integration of Myo Armbands and an android-based mobile application for communication with hearing-impaired persons. Paper presented at the 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).
  • Wahid, M. F., Tafreshi, R., Al-Sowaidi, M., & Langari, R. (2018). Subject-independent hand gesture recognition using normalization and machine learning algorithms. Journal of computational science, 27, 69-76.
  • Wibawa, A. D., & Sumpeno, S. (2017). Gesture Recognition for Indonesian Sign Language Systems (ISLS) Using Multimodal Sensor Leap Motion and Myo Armband Controllers Based-on Naïve Bayes Classifier. Paper presented at the 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT).
  • Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9), 2839-2846.
  • Yang, J., Pan, J., & Li, J. (2017). sEMG-based continuous hand gesture recognition using GMM-HMM and threshold model. Paper presented at the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Cengiz Tepe 0000-0003-4065-5207

Mehmet Can Demir Bu kişi benim 0000-0002-2372-4242

Yayımlanma Tarihi 15 Ağustos 2020
Yayımlandığı Sayı Yıl 2020 Ejosat Özel Sayı 2020 (HORA)

Kaynak Göster

APA Tepe, C., & Demir, M. C. (2020). Detection and Classification of Muscle Activation in EMG Data Acquired by Myo Armband. Avrupa Bilim Ve Teknoloji Dergisi178-183. https://doi.org/10.31590/ejosat.779660