Araştırma Makalesi
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Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü

Yıl 2020, Cilt: 8 Sayı: 1, 233 - 245, 31.01.2020
https://doi.org/10.29130/dubited.606622

Öz

Bu çalışma kapsamında insansız bir kara aracının kişinin el ve parmak hareketleri ile uzaktan kontrolü gerçekleştirilmiştir. Beyinden kol kaslarına iletilen ve kişinin el hareketlerini gerçekleştirmesini sağlayan Elektromiyografi (EMG) sinyalleri, kişinin koluna giydiği sekiz EMG sensör içeren bileklik vasıtası ile gerçek zamanlı olarak alınmıştır. Raspberry pi 3 gömülü sistem kartı üzerinde geliştirilen sinyal işleme, öznitelik çıkarımı ve sınıflandırma algoritmaları kullanılarak anlamlandırılmıştır. Başka bir deyişle el hareketin örüntüsü (el kapama, parmak açma, serçe parmak temas, bilek dışa bükme, vs.) ile EMG sinyal grubu arasındaki ilişkiler tanımlanmıştır. Anlamlandırılan her bir el hareketi araç için bir hareketi kontrol komutu (el kapama: araç ileri, parmak açma: araç dur, serçe parmağa temas: sola dönüş, bilek dışa bükme: sağa dönüş, vs.) olarak kullanılmıştır. Böylece insan – mobil araç etkileşim ağı kurulmuştur. Kurulan insan- mobil araç etkileşim ağı sayesinde el hareketleri ile mobil aracın gerçek zamanlı hareket kontrolü ortalama % 92 başarı ile gerçekleştirilmiştir.

Teşekkür

Bu çalışma 2209-A TÜBİTAK- Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında desteklenmiştir. Ayrıca bu proje TUBITAK 2242 Lisans Projeleri Bölge Yarışmalarında Bilişim Teknolojileri alanında ikincilik ödülüne layık görülmüştür.

Kaynakça

  • [1] B. K. Chakraborty, D. Sarma, M. K. Bhuyan, K. F. MacDorman, “Review of constraints on vision-based gesture Recognition for human-computer interaction”. IET Computer Vision, vol.12, pp. 3–15, 2017.
  • [2] A. Pasarica, C. Miron, D. Arotaritei, G. Andruseac, H. Costin, “Rotariu, Remote control of a robotic platform based on hand gesture recognition”, In Proceedings of the E-Health and Bioengineering Conference (EHB), Sinaia, Romania, 22–24 June 2017; pp. 643–646.
  • [3] H. Abualola, H. Al Ghothani, A.N. Eddin, N. Almoosa, K. Poon, “Flexible gesture recognition using wearable inertial sensors”. In Proceedings of the IEEE 59th International Midwest Symposium on CircuitsandSystems (MWSCAS), Abu Dhabi, UAE, 16–19 October 2016; pp. 1–4.
  • [4] A.I. Maqueda, C.R. del-Blanco, F. Jaureguizar, N. García, “Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns”, Computer Vision and Image Understanding, vol.141, pp.126–137, 2015.
  • [5] S.A. Rahman, I. Song, M.K. Leung, I. Lee, K. Lee, “Fast action recognition using negative space features”. Expert System Appication, vol. 41, pp. 574–587, 2014.
  • [6] V. Gandhi, T.M. McGinnity, “Quantum neural network-based surface EMG signal filtering for control of robotic hand”. In Proceedings of the IEEE International Joint Conference on Neural Networks, Dallas, TX, USA, 4–9 August 2013.
  • [7] I. Moon, M. Lee, J. Ryu, M. Mun, “Intelligent robotic wheelchair with EMG, gesture, and voice-based interfaces”. In Proceedings of the IEEE/RSJ International Conference on IntelligentRobotsandSystems (IROS 2003), LasVegas, NV, USA, 27–31 October 2003; pp. 3453–3458.
  • [8] G. Kucukyildiz, H. Ocak, S. Karakaya, O. Sayli, “Design and implementation of a multi-sensor based brain-computer interface for a robotic wheelchair”. Journal of Intelligent and Robotic Systems, vol. 87, pp. 247–263, 2017.
  • [9] S. Shin, D. Kim, Y. Seo, “Controlling mobile robot using IMU and EMG sensor-based gesture recognition”. In Proceedings of the Ninth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), Guangdong, China, 8–10 November 2014; pp. 554–557.
  • [10] G.C. Luh, H.A. Lin, Y.H. Ma, C.J. Yen, “Intuitive muscle-gesture based robot navigation control using wearable gesture armband”. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), Guangzhou, China, 12–15 July 2015; pp. 389–395.
  • [11] V. Gandhi, “Brain-Computer Interfacing for Assistive Robotics: Electroencephalograms, Recurrent Quantum Neural Networks, and User-Centric Graphical Interfaces”, AcademicPress: Cambridge, MA, USA, 2014.
  • [12] V. Gandhi, G. Prasad, D. Coyle, L. Behera, T.M. McGinnity, “EEG based mobile robot control through an adaptive brain-robot interface”, IEEE Transactions on Systems, Man, and Cybernetics: Systems. vol. 44, pp. 1278–1285, 2014.
  • [13] V. Gandhi, G. Prasad, D. Coyle, L. Behera, T.M. McGinnity, “Quantum neural network-based EEG filteringfor a Brain-computer interface”. IEEE Transactions on Neural Networks and Learning Systems, vol. 25, pp. 278–288, 2014.
  • [14] I. Rodriguez, A. Malanda, L. Gila, “Filter design for cancellation of baseline – fluctuation in needle EMG recordings”, Computer Methods and Programs in Biomedicine, vol. 81, pp. 79-93, 2006.
  • [15] M.B.I. Raez, M.S. Hussain, and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications”, Biological Procedures Online, vol.8, pp. 11–35, 2006.
  • [16] A.P. Dobrowolski, M. Wierzbowski, K. Tomczykiewicz, “Multi-resolution MÜAPs decomposition and SVM based analysis in the classification of neuromuscular disorders”, Computer Methods and Programs in Biomedicine, Elsevier, 2010.
  • [17] N. D. Pagnagiotacopulos, J. S. Lae, M. H. Pope, “Evaluation of EMG signals from rehabilitated patients with low back pain using wavelets”, J. Electromyography and Kinesiology, vol. 8, pp.269 – 278, 1998.
  • [18] B. Hudgins, P. Parker, R.N. Scott, “A new strategy for multifunction myoelectric control”, IEEE Transactions on Biomedical Engineering, vol. 40, pp. 82–94, 1993.
  • [19] K. Englehart, B. Hugdins, P. Parker, “Multifunction Control of Prostheses Using the Myoelectric Signal”. In Intelligent Systems and Technologies in Rehabilitation Engineering; Teodorescu, H.-N.L. Jain, L.C. Eds.; CRC Press: New York, NY, USA, 2000.
  • [20] K. Englehart, B. Hudgins, “A robust, real-time control scheme for multifunction myoelectric control”. IEEE Transactions on Biomedical Engineering. vol. 50, pp. 848–854, 2003.
  • [21] M. B. I. Reaz, M. S. Hussain and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications”, Biological Procedures Online, vol:8(1), pp.11-3, doi:10.1251/bpo115, March 23, 2006.
  • [22] M.J. Islam, Q.J. Wu, M. Ahmadi, M.A. Sid-Ahmed, “Investigating the performance of naive-bayes classifiers and k-nearest neighbor classifiers”. InProceedings of the International Conference on Convergence Information Technology, Gyeongju, Korea, 21–23 November 2007; pp. 1541–1546.

Remote Control of Unmanned Ground Vehicle via Myo-Electrical Signals

Yıl 2020, Cilt: 8 Sayı: 1, 233 - 245, 31.01.2020
https://doi.org/10.29130/dubited.606622

Öz

In this study, remote control of an unmanned land vehicle by hand and finger movements was performed. Electromyography (EMG) signals, which are transmitted from the brain to the arm muscles and enable the person to perform hand movements, were received in real-time by a wristband containing eight EMG sensors worn on the arm. The signal processing, developed on the Raspberry pi 3 embedded system board, was recognized by using feature extraction and classification algorithms. In other words, the relationship between the pattern of hand movement (hand closure,hand opening, thumb-pinky finger touch, wrist bending, etc.) and the EMG signal group is defined. For each recognized hand gesture was used as a motion control command for the vehicle (hand closure: vehicle forward, hand opening: vehicle stop, thumb-little finger touch: left turn, wrist bend: right turn, etc.). Thus, a human - mobile vehicle interaction network was established. Thanks to the established human-mobile vehicle interaction network, real-time motion control of hand movements and the mobile vehicle were achieved with an average success rate of 92%.

Kaynakça

  • [1] B. K. Chakraborty, D. Sarma, M. K. Bhuyan, K. F. MacDorman, “Review of constraints on vision-based gesture Recognition for human-computer interaction”. IET Computer Vision, vol.12, pp. 3–15, 2017.
  • [2] A. Pasarica, C. Miron, D. Arotaritei, G. Andruseac, H. Costin, “Rotariu, Remote control of a robotic platform based on hand gesture recognition”, In Proceedings of the E-Health and Bioengineering Conference (EHB), Sinaia, Romania, 22–24 June 2017; pp. 643–646.
  • [3] H. Abualola, H. Al Ghothani, A.N. Eddin, N. Almoosa, K. Poon, “Flexible gesture recognition using wearable inertial sensors”. In Proceedings of the IEEE 59th International Midwest Symposium on CircuitsandSystems (MWSCAS), Abu Dhabi, UAE, 16–19 October 2016; pp. 1–4.
  • [4] A.I. Maqueda, C.R. del-Blanco, F. Jaureguizar, N. García, “Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns”, Computer Vision and Image Understanding, vol.141, pp.126–137, 2015.
  • [5] S.A. Rahman, I. Song, M.K. Leung, I. Lee, K. Lee, “Fast action recognition using negative space features”. Expert System Appication, vol. 41, pp. 574–587, 2014.
  • [6] V. Gandhi, T.M. McGinnity, “Quantum neural network-based surface EMG signal filtering for control of robotic hand”. In Proceedings of the IEEE International Joint Conference on Neural Networks, Dallas, TX, USA, 4–9 August 2013.
  • [7] I. Moon, M. Lee, J. Ryu, M. Mun, “Intelligent robotic wheelchair with EMG, gesture, and voice-based interfaces”. In Proceedings of the IEEE/RSJ International Conference on IntelligentRobotsandSystems (IROS 2003), LasVegas, NV, USA, 27–31 October 2003; pp. 3453–3458.
  • [8] G. Kucukyildiz, H. Ocak, S. Karakaya, O. Sayli, “Design and implementation of a multi-sensor based brain-computer interface for a robotic wheelchair”. Journal of Intelligent and Robotic Systems, vol. 87, pp. 247–263, 2017.
  • [9] S. Shin, D. Kim, Y. Seo, “Controlling mobile robot using IMU and EMG sensor-based gesture recognition”. In Proceedings of the Ninth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), Guangdong, China, 8–10 November 2014; pp. 554–557.
  • [10] G.C. Luh, H.A. Lin, Y.H. Ma, C.J. Yen, “Intuitive muscle-gesture based robot navigation control using wearable gesture armband”. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), Guangzhou, China, 12–15 July 2015; pp. 389–395.
  • [11] V. Gandhi, “Brain-Computer Interfacing for Assistive Robotics: Electroencephalograms, Recurrent Quantum Neural Networks, and User-Centric Graphical Interfaces”, AcademicPress: Cambridge, MA, USA, 2014.
  • [12] V. Gandhi, G. Prasad, D. Coyle, L. Behera, T.M. McGinnity, “EEG based mobile robot control through an adaptive brain-robot interface”, IEEE Transactions on Systems, Man, and Cybernetics: Systems. vol. 44, pp. 1278–1285, 2014.
  • [13] V. Gandhi, G. Prasad, D. Coyle, L. Behera, T.M. McGinnity, “Quantum neural network-based EEG filteringfor a Brain-computer interface”. IEEE Transactions on Neural Networks and Learning Systems, vol. 25, pp. 278–288, 2014.
  • [14] I. Rodriguez, A. Malanda, L. Gila, “Filter design for cancellation of baseline – fluctuation in needle EMG recordings”, Computer Methods and Programs in Biomedicine, vol. 81, pp. 79-93, 2006.
  • [15] M.B.I. Raez, M.S. Hussain, and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications”, Biological Procedures Online, vol.8, pp. 11–35, 2006.
  • [16] A.P. Dobrowolski, M. Wierzbowski, K. Tomczykiewicz, “Multi-resolution MÜAPs decomposition and SVM based analysis in the classification of neuromuscular disorders”, Computer Methods and Programs in Biomedicine, Elsevier, 2010.
  • [17] N. D. Pagnagiotacopulos, J. S. Lae, M. H. Pope, “Evaluation of EMG signals from rehabilitated patients with low back pain using wavelets”, J. Electromyography and Kinesiology, vol. 8, pp.269 – 278, 1998.
  • [18] B. Hudgins, P. Parker, R.N. Scott, “A new strategy for multifunction myoelectric control”, IEEE Transactions on Biomedical Engineering, vol. 40, pp. 82–94, 1993.
  • [19] K. Englehart, B. Hugdins, P. Parker, “Multifunction Control of Prostheses Using the Myoelectric Signal”. In Intelligent Systems and Technologies in Rehabilitation Engineering; Teodorescu, H.-N.L. Jain, L.C. Eds.; CRC Press: New York, NY, USA, 2000.
  • [20] K. Englehart, B. Hudgins, “A robust, real-time control scheme for multifunction myoelectric control”. IEEE Transactions on Biomedical Engineering. vol. 50, pp. 848–854, 2003.
  • [21] M. B. I. Reaz, M. S. Hussain and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications”, Biological Procedures Online, vol:8(1), pp.11-3, doi:10.1251/bpo115, March 23, 2006.
  • [22] M.J. Islam, Q.J. Wu, M. Ahmadi, M.A. Sid-Ahmed, “Investigating the performance of naive-bayes classifiers and k-nearest neighbor classifiers”. InProceedings of the International Conference on Convergence Information Technology, Gyeongju, Korea, 21–23 November 2007; pp. 1541–1546.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Beyda Taşar 0000-0002-4689-8579

Ahmet Burak Tatar Bu kişi benim 0000-0001-5848-443X

Özgür Nazlı Bu kişi benim 0000-0003-2211-3228

Osman Kalkan Bu kişi benim 0000-0002-1386-1139

Yayımlanma Tarihi 31 Ocak 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 1

Kaynak Göster

APA Taşar, B., Tatar, A. B., Nazlı, Ö., Kalkan, O. (2020). Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 8(1), 233-245. https://doi.org/10.29130/dubited.606622
AMA Taşar B, Tatar AB, Nazlı Ö, Kalkan O. Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü. DÜBİTED. Ocak 2020;8(1):233-245. doi:10.29130/dubited.606622
Chicago Taşar, Beyda, Ahmet Burak Tatar, Özgür Nazlı, ve Osman Kalkan. “Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 8, sy. 1 (Ocak 2020): 233-45. https://doi.org/10.29130/dubited.606622.
EndNote Taşar B, Tatar AB, Nazlı Ö, Kalkan O (01 Ocak 2020) Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 8 1 233–245.
IEEE B. Taşar, A. B. Tatar, Ö. Nazlı, ve O. Kalkan, “Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü”, DÜBİTED, c. 8, sy. 1, ss. 233–245, 2020, doi: 10.29130/dubited.606622.
ISNAD Taşar, Beyda vd. “Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 8/1 (Ocak 2020), 233-245. https://doi.org/10.29130/dubited.606622.
JAMA Taşar B, Tatar AB, Nazlı Ö, Kalkan O. Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü. DÜBİTED. 2020;8:233–245.
MLA Taşar, Beyda vd. “Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, c. 8, sy. 1, 2020, ss. 233-45, doi:10.29130/dubited.606622.
Vancouver Taşar B, Tatar AB, Nazlı Ö, Kalkan O. Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü. DÜBİTED. 2020;8(1):233-45.