Implementasi Convolutional Neural Network Pada Lengan Prostetik Bionik Berbasis Wearable Electromyography Armband Sensor
Kata Kunci:
Penyandang Disabilitas, Convolutional Neural Network (CNN), Lengan Prostetik Bionik, Electromyography (EMG), Sensor Oymotion gForce200, Real-time SistemAbstrak
Kehilangan tangan akibat amputasi dapat mempengaruhi kesehatan fisiologis dan psikologis penderitanya. Jumlah penyandang disabilitas di Indonesia mencapai 22,97 juta jiwa, sekitar 8,5% dari total penduduk Indonesia. Jumlah tersebut terhitung cukup besar sehingga diperlukan pengembangan teknologi untuk membantu pasien disabilitas, salah satunya tangan prostetik bionik. Penelitian sebelumnya mengembangkan lengan prostetik bionik berbasis sensor berkabel Myoware V2 namun masih terdapat banyak noise dan tantangan dalam akurasi gerakan. Penelitian ini bertujuan untuk mengembangkan sistem lengan prostetik bionik dengan implementasi Convolutional Neural Network (CNN) berbasis Wearable Electromyography Armband Sensor. Tipe sensor yang digunakan adalah sensor Oymotion gForce200 Armband Gesture. . Penelitian ini menggunakan empat fitur yaitu Root Mean Square (RMS), Waveform Length (WL), Mean Average Value (MAV), dan Amplitude First Burst (AFB) dan digunakan sebagai input untuk klasifikasi Convolutional Neural Network (CNN). Hasil pengujian sensor memiliki akurasi sebesar 100% dengan nilai yang sesuai yaitu 0-3,3 V. Selanjutnya, hasil pelatihan akurasi model CNN sebesar 94,83% dengan learning rate 0,1 dan 100 epochs. Sensor Oymotion gForce200 berhasil mengklasifikasikan gerakan pada sistem dengan akurasi 80%, meskipun masih ada tantangan terkait karakteristik sinyal individu. Waktu komputasi untuk setiap gerakan adalah 0,046 detik, menunjukkan bahwa sistem ini memenuhi kriteria untuk digunakan secara real-time dalam aplikasi prostetik.
Referensi
Abu, M.A., Rosleesham, S., Suboh, M.Z., Yid, M.S.M., Kornain, Z. and Jamaluddin, N.F., 2020. Classification of EMG signal for multiple hand gestures based on neural network. Indonesian Journal of Electrical Engineering and Computer Science, 17(1), pp.256-263.
Alves Maia de Souza, G., Gomes Netto Monte da Silva, M. and Fontes da Gama, A.E., 2019. Upper limb muscle activation: an EMG analysis using Myo® armband. In XXVI Brazilian Congress on Biomedical Engineering: CBEB 2018, Armação de Buzios, RJ, Brazil, 21-25 October 2018 (Vol. 1) (pp. 397-404). Springer Singapore.
Amirullah, M.F., Kuswanto, D. and Krisbianto, A.D., 2021. Desain Lengan Bionik Berbasis Open Source (HACKberry Arm) untuk Anak-Anak Tunadaksa Amputasi Trans-radial agar Lebih Percaya Diri. Institut Teknologi Sepuluh Nopember.
Arozi, M., Ariyanto, M., Kristianto, A. and Setiawan, J.D., 2020, September. 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). IEEE. 10.1109/ICITACEE50144.2020.9239169
Asif, A.R., Waris, A., Gilani, S.O., Jamil, M., Ashraf, H., Shafique, M. and Niazi, I.K., 2020. Performance evaluation of convolutional neural network for hand gesture recognition using EMG. Sensors, 20(6), p.1642. https://doi.org/10.3390/s20061642.
Babu, D., Nasir, A., Farag, M., Sidik, M.M. and Rejab, S.B.M., 2022, March. Development of prosthetic robotic arm with patient monitoring system for disabled children; preliminary results. In 2022 9th international conference on electrical and electronics engineering (ICEEE) (pp. 206-212). IEEE.
Bakırcıoğlu, K. and Özkurt, N., 2020. Classification of EMG signals using convolution neural network. International Journal of Applied Mathematics Electronics and Computers, 8(4), pp.115-119.
Basumatary, H. and Hazarika, S.M., 2020. State of the art in bionic hands. IEEE Transactions on Human-Machine Systems, 50(2), pp.116-130. 10.1109/THMS.2020.2970740
Bin Yakob, M.Y., bin Baharuddin, M.Z., Khairudin, A.R.M. and Karim, M.H.B.A., 2021, June. Telecontrol of Prosthetic Robot Hand Using Myo Armband. In 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) (pp. 288-293). IEEE. 10.1109/I2CACIS52118.2021.9495919
Chai, T., 2022. Root Mean Square. In Encyclopedia of Mathematical Geosciences (pp. 1-3). Cham: Springer International Publishing.
Chopra, T., 2022. Application of Artificial Intelligence and 3D Printing in Prosthetics: A Review. NeuroQuantology, 20(13), p.2691.
Du, Y., Jin, J., Wang, Q. and Fan, J., 2022, May. EMG-based continuous motion decoding of upper limb with spiking neural network. In 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1-5). IEEE.
Dunai, L., Novak, M. and García Espert, C., 2020. Human hand anatomy-based prosthetic hand. Sensors, 21(1), p.137. https://doi.org/10.3390/s21010137
Farag, H.O., Awad, M.I., Baioumy, A.M., Shawky, A.A., Taha, A.A., Hassan, A.A., Elqess, Y.A., Ismail, O.I., El-Shazly, B.T., Mohamed, Y.A. and Salem, S.M., 2021, December. Electromyography Signal Classification using Convolution Neural Network Architecture for Bionic Arm High Level Control. In 2021 16th International Conference on Computer Engineering and Systems (ICCES) (pp. 1-6). IEEE. 10.1109/ICCES54031.2021.9686171
French, F., Terry, C., Huq, S., Furieri, I., Jarzembinski, M., Pauliukenas, S., Morrison, N. and Shepherd, K., 2022. Expressive interaction design using facial muscles as controllers. Multimodal Technologies and Interaction, 6(9), p.78.
Gohel, V. and Mehendale, N., 2020. Review on electromyography signal acquisition and processing. Biophysical reviews, 12(6), pp.1361-1367.
Gomez-Correa, M. and Cruz-Ortiz, D., 2022. Low-cost wearable band sensors of surface electromyography for detecting hand movements. Sensors, 22(16), p.5931.
Hassan, H.F., Abou-Loukh, S.J. and Ibraheem, I.K., 2020. Teleoperated robotic arm movement using electromyography signal with wearable Myo armband. Journal of King Saud University-Engineering Sciences, 32(6), pp.378-387. https://doi.org/10.1016/j.jksues.2019.05.001
Kadavath, M.R.K., Nasor, M. and Imran, A., 2024. Enhanced hand gesture recognition with surface electromyogram and machine learning. Sensors, 24(16), p.5231. https://doi.org/10.3390/s24165231
Kok, C.L., Ho, C.K., Tan, F.K. and Koh, Y.Y., 2024. Machine learning-based feature extraction and classification of emg signals for intuitive prosthetic control. Applied Sciences, 14(13), p.5784. https://doi.org/10.3390/app14135784
Kurniawan, E.D., Muthiah, S. and Simbolon, D.E., 2023, November. Development of Controllable LED Light Source for Retinomorphic Sensor Measurement Test. In 2023 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) (pp. 113-117). IEEE. 10.1109/ICRAMET60171.2023.10366655
Li, W., Shi, P. and Yu, H., 2021. Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future. Frontiers in neuroscience, 15, p.621885.https://doi.org/10.3389/fnins.2021.621885
López, L.I.B., Ferri, F.M., Zea, J., Caraguay, Á.L.V. and Benalcázar, M.E., 2024. CNN-LSTM and post-processing for EMG-based hand gesture recognition. Intelligent Systems with Applications, 22, p.200352. https://doi.org/10.1016/j.iswa.2024.200352
Mir, S. and Dhawan, N., 2022. A comprehensive review on the recycling of discarded printed circuit boards for resource recovery. Resources, Conservation and Recycling, 178, p.106027. https://doi.org/10.1016/j.resconrec.2021.106027
Lu, L., Mao, J., Wang, W., Ding, G. and Zhang, Z., 2020. A study of personal recognition method based on EMG signal. IEEE Transactions on Biomedical Circuits and Systems, 14(4), pp.681-691. 10.1109/TBCAS.2020.3005148
Patel, B.J., 2022. Design and Development of 3D-Printed Prosthetic Arm with Touch Sensing Technology for Improved User Control.
Prasetyo, D.D., Wicaksono, J.W. and Yuandari, A., 2023. RANCANG BANGUN DAN OPTIMALISASI LENGAN ROBOT BIONIK BERBASIS MIKROKONTROLER ARDUINO. IMDeC, 5.
Putra, A.K., Khusnuliawati, H. and Al Haris, F.H.S., 2020. Rancang Bangun Tangan Prosthesis Menggunakan Flex Sensor dan Modul Bluetooth Berbasis Arduino (Doctoral dissertation, Universitas Sahid Surakarta).
Sabbatini, M., 2024. Hardening IoT Devices: An Analysis of the ESP32 Microcontroller.
Savithri, C.N., Priya, E. and Sudharsanan, J., 2021. Classification of semg signal-based arm action using convolutional neural network. Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems, pp.241-259. https://doi.org/10.1007/978-981-15-6141-2_13
Segura-Garcia, J., Sturley, S., Arevalillo-Herraez, M., Alcaraz-Calero, J.M., Felici-Castell, S. and Navarro-Camba, E.A., 2024. 5G AI-IoT system for bird species monitoring and song classification. Sensors, 24(11), p.3687.
Sulistyorini, T., Sofi, N. and Sova, E., 2022. Pemanfaatan Nodemcu Esp8266 Berbasis Android (Blynk) Sebagai Alat Alat Mematikan Dan Menghidupkan Lampu. Jurnal Ilmiah Teknik, 1(3), pp.40-53.
Tepe, C. and Demir, M.C., 2022. Real-time classification of emg myo armband data using support vector machine. IRBM, 43(4), pp.300-308. https://doi.org/10.1016/j.irbm.2022.06.001
Tresnaasih, I., 2020. Modul pembelajaran biologi SMA kelas XI: sistem gerak pada manusia.
Turgunov, A., Zohirov, K., Ganiyev, A. and Sharopova, B., 2020, May. Defining the features of EMG signals on the forearm of the hand using SVM, RF, k-NN classification algorithms. In 2020 Information Communication Technologies Conference (ICTC) (pp. 260-264). IEEE.
Waris, A. and Kamavuako, E.N., 2018. Effect of threshold values on the combination of EMG time domain features: Surface versus intramuscular EMG. Biomedical Signal Processing and Control, 45, pp.267-273.
Wijaya, A.P., 2024. Pengembangan Sistem Pengenalan Pergerakan Prostetik Tangan Bionik Menggunakan Metode K-Nearest Neighbor dengan Fitur Power Spectral Density. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 8(6).
Xu, J., Liu, B., Wang, X. and Hu, D., 2016. Computational model of 18650 lithium-ion battery with coupled strain rate and SOC dependencies. Applied Energy, 172, pp.180-189. https://doi.org/10.1016/j.apenergy.2016.03.108
Yusri, R., Edriati, S. and Yuhendri, R., 2020. Pelatihan Microsoft Office Excel Sebagai Upaya Peningkatan Kemampuan Mahasiswa Dalam Mengolah Data. Rangkiang: Jurnal Pengabdian Pada Masyarakat, 2(1), pp.32-37.
Zhang, Z., Han, T., Huang, C. and Shuai, C., 2024. Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System. Electronics, 13(2), p.454.https://doi.org/10.3390/electronics13020454
Abu, M.A., Rosleesham, S., Suboh, M.Z., Yid, M.S.M., Kornain, Z. and Jamaluddin, N.F., 2020. Classification of EMG signal for multiple hand gestures based on neural network. Indonesian Journal of Electrical Engineering and Computer Science, 17(1), pp.256-263.
Alves Maia de Souza, G., Gomes Netto Monte da Silva, M. and Fontes da Gama, A.E., 2019. Upper limb muscle activation: an EMG analysis using Myo® armband. In XXVI Brazilian Congress on Biomedical Engineering: CBEB 2018, Armação de Buzios, RJ, Brazil, 21-25 October 2018 (Vol. 1) (pp. 397-404). Springer Singapore.
Amirullah, M.F., Kuswanto, D. and Krisbianto, A.D., 2021. Desain Lengan Bionik Berbasis Open Source (HACKberry Arm) untuk Anak-Anak Tunadaksa Amputasi Trans-radial agar Lebih Percaya Diri. Institut Teknologi Sepuluh Nopember.
Arozi, M., Ariyanto, M., Kristianto, A. and Setiawan, J.D., 2020, September. 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). IEEE. 10.1109/ICITACEE50144.2020.9239169
Asif, A.R., Waris, A., Gilani, S.O., Jamil, M., Ashraf, H., Shafique, M. and Niazi, I.K., 2020. Performance evaluation of convolutional neural network for hand gesture recognition using EMG. Sensors, 20(6), p.1642. https://doi.org/10.3390/s20061642.
Babu, D., Nasir, A., Farag, M., Sidik, M.M. and Rejab, S.B.M., 2022, March. Development of prosthetic robotic arm with patient monitoring system for disabled children; preliminary results. In 2022 9th international conference on electrical and electronics engineering (ICEEE) (pp. 206-212). IEEE.
Bakırcıoğlu, K. and Özkurt, N., 2020. Classification of EMG signals using convolution neural network. International Journal of Applied Mathematics Electronics and Computers, 8(4), pp.115-119.
Basumatary, H. and Hazarika, S.M., 2020. State of the art in bionic hands. IEEE Transactions on Human-Machine Systems, 50(2), pp.116-130. 10.1109/THMS.2020.2970740
Bin Yakob, M.Y., bin Baharuddin, M.Z., Khairudin, A.R.M. and Karim, M.H.B.A., 2021, June. Telecontrol of Prosthetic Robot Hand Using Myo Armband. In 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) (pp. 288-293). IEEE. 10.1109/I2CACIS52118.2021.9495919
Chai, T., 2022. Root Mean Square. In Encyclopedia of Mathematical Geosciences (pp. 1-3). Cham: Springer International Publishing.
Chopra, T., 2022. Application of Artificial Intelligence and 3D Printing in Prosthetics: A Review. NeuroQuantology, 20(13), p.2691.
Du, Y., Jin, J., Wang, Q. and Fan, J., 2022, May. EMG-based continuous motion decoding of upper limb with spiking neural network. In 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1-5). IEEE.
Dunai, L., Novak, M. and García Espert, C., 2020. Human hand anatomy-based prosthetic hand. Sensors, 21(1), p.137. https://doi.org/10.3390/s21010137
Farag, H.O., Awad, M.I., Baioumy, A.M., Shawky, A.A., Taha, A.A., Hassan, A.A., Elqess, Y.A., Ismail, O.I., El-Shazly, B.T., Mohamed, Y.A. and Salem, S.M., 2021, December. Electromyography Signal Classification using Convolution Neural Network Architecture for Bionic Arm High Level Control. In 2021 16th International Conference on Computer Engineering and Systems (ICCES) (pp. 1-6). IEEE. 10.1109/ICCES54031.2021.9686171
French, F., Terry, C., Huq, S., Furieri, I., Jarzembinski, M., Pauliukenas, S., Morrison, N. and Shepherd, K., 2022. Expressive interaction design using facial muscles as controllers. Multimodal Technologies and Interaction, 6(9), p.78.
Gohel, V. and Mehendale, N., 2020. Review on electromyography signal acquisition and processing. Biophysical reviews, 12(6), pp.1361-1367.
Gomez-Correa, M. and Cruz-Ortiz, D., 2022. Low-cost wearable band sensors of surface electromyography for detecting hand movements. Sensors, 22(16), p.5931.
Hassan, H.F., Abou-Loukh, S.J. and Ibraheem, I.K., 2020. Teleoperated robotic arm movement using electromyography signal with wearable Myo armband. Journal of King Saud University-Engineering Sciences, 32(6), pp.378-387. https://doi.org/10.1016/j.jksues.2019.05.001
Kadavath, M.R.K., Nasor, M. and Imran, A., 2024. Enhanced hand gesture recognition with surface electromyogram and machine learning. Sensors, 24(16), p.5231. https://doi.org/10.3390/s24165231
Kok, C.L., Ho, C.K., Tan, F.K. and Koh, Y.Y., 2024. Machine learning-based feature extraction and classification of emg signals for intuitive prosthetic control. Applied Sciences, 14(13), p.5784. https://doi.org/10.3390/app14135784
Kurniawan, E.D., Muthiah, S. and Simbolon, D.E., 2023, November. Development of Controllable LED Light Source for Retinomorphic Sensor Measurement Test. In 2023 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET) (pp. 113-117). IEEE. 10.1109/ICRAMET60171.2023.10366655
Li, W., Shi, P. and Yu, H., 2021. Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future. Frontiers in neuroscience, 15, p.621885.https://doi.org/10.3389/fnins.2021.621885
López, L.I.B., Ferri, F.M., Zea, J., Caraguay, Á.L.V. and Benalcázar, M.E., 2024. CNN-LSTM and post-processing for EMG-based hand gesture recognition. Intelligent Systems with Applications, 22, p.200352. https://doi.org/10.1016/j.iswa.2024.200352
Mir, S. and Dhawan, N., 2022. A comprehensive review on the recycling of discarded printed circuit boards for resource recovery. Resources, Conservation and Recycling, 178, p.106027. https://doi.org/10.1016/j.resconrec.2021.106027
Lu, L., Mao, J., Wang, W., Ding, G. and Zhang, Z., 2020. A study of personal recognition method based on EMG signal. IEEE Transactions on Biomedical Circuits and Systems, 14(4), pp.681-691. 10.1109/TBCAS.2020.3005148
Patel, B.J., 2022. Design and Development of 3D-Printed Prosthetic Arm with Touch Sensing Technology for Improved User Control.
Prasetyo, D.D., Wicaksono, J.W. and Yuandari, A., 2023. RANCANG BANGUN DAN OPTIMALISASI LENGAN ROBOT BIONIK BERBASIS MIKROKONTROLER ARDUINO. IMDeC, 5.
Putra, A.K., Khusnuliawati, H. and Al Haris, F.H.S., 2020. Rancang Bangun Tangan Prosthesis Menggunakan Flex Sensor dan Modul Bluetooth Berbasis Arduino (Doctoral dissertation, Universitas Sahid Surakarta).
Sabbatini, M., 2024. Hardening IoT Devices: An Analysis of the ESP32 Microcontroller.
Savithri, C.N., Priya, E. and Sudharsanan, J., 2021. Classification of semg signal-based arm action using convolutional neural network. Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems, pp.241-259. https://doi.org/10.1007/978-981-15-6141-2_13
Segura-Garcia, J., Sturley, S., Arevalillo-Herraez, M., Alcaraz-Calero, J.M., Felici-Castell, S. and Navarro-Camba, E.A., 2024. 5G AI-IoT system for bird species monitoring and song classification. Sensors, 24(11), p.3687.
Sulistyorini, T., Sofi, N. and Sova, E., 2022. Pemanfaatan Nodemcu Esp8266 Berbasis Android (Blynk) Sebagai Alat Alat Mematikan Dan Menghidupkan Lampu. Jurnal Ilmiah Teknik, 1(3), pp.40-53.
Tepe, C. and Demir, M.C., 2022. Real-time classification of emg myo armband data using support vector machine. IRBM, 43(4), pp.300-308. https://doi.org/10.1016/j.irbm.2022.06.001
Tresnaasih, I., 2020. Modul pembelajaran biologi SMA kelas XI: sistem gerak pada manusia.
Turgunov, A., Zohirov, K., Ganiyev, A. and Sharopova, B., 2020, May. Defining the features of EMG signals on the forearm of the hand using SVM, RF, k-NN classification algorithms. In 2020 Information Communication Technologies Conference (ICTC) (pp. 260-264). IEEE.
Waris, A. and Kamavuako, E.N., 2018. Effect of threshold values on the combination of EMG time domain features: Surface versus intramuscular EMG. Biomedical Signal Processing and Control, 45, pp.267-273.
Wijaya, A.P., 2024. Pengembangan Sistem Pengenalan Pergerakan Prostetik Tangan Bionik Menggunakan Metode K-Nearest Neighbor dengan Fitur Power Spectral Density. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 8(6).
Xu, J., Liu, B., Wang, X. and Hu, D., 2016. Computational model of 18650 lithium-ion battery with coupled strain rate and SOC dependencies. Applied Energy, 172, pp.180-189. https://doi.org/10.1016/j.apenergy.2016.03.108
Yusri, R., Edriati, S. and Yuhendri, R., 2020. Pelatihan Microsoft Office Excel Sebagai Upaya Peningkatan Kemampuan Mahasiswa Dalam Mengolah Data. Rangkiang: Jurnal Pengabdian Pada Masyarakat, 2(1), pp.32-37.
Zhang, Z., Han, T., Huang, C. and Shuai, C., 2024. Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System. Electronics, 13(2), p.454.https://doi.org/10.3390/electronics13020454
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