Pengoptimalan Gerakan Lengan Prostetik Bionik Menggunakan Decision Based Velocity Ramp

Penulis

  • Edita Rosana Widasari Universitas Brawijaya, Malang

DOI:

https://doi.org/10.25126/jtiik.2025128535

Kata Kunci:

DBVR, EMG, Gerakan Lengan Prostetik Bionik, servo, sudut

Abstrak

Di seluruh dunia, terdapat 57,7 juta individu yang kehilangan lengan mereka dan menghadapi hambatan dalam melakukan tugas sehari-hari. Saat ini, lengan prostetik bionik telah dikembangkan salah satunya yaitu "Sistem Pengenalan Pergerakan Prostetik Tangan Bionik Bawah Siku Menggunakan Metode K-Nearest Neighbor berbasis sinyal Electromyography" (Adani, M. S., Widasari, E.R., 2023). Namun, penelitian tersebut memiliki beberapa keterbatasan dalam kecepatan dan posisi sudut servo. Untuk mengatasi masalah ini, penelitian ini mengusulkan penggunaan Decision Based Velocity Ramp (DBVR) sebagai metode post-processing untuk memperbaiki kecepatan servo dalam mencapai posisi target dan meningkatkan presisi sudut posisi target servo. Hasil menunjukkan bahwa pembacaan sensor Myoware memiliki tingkat akurasi yang sangat tinggi hingga 100%, dengan nilai minimum 0.18V dan maksimum 2.84V untuk seluruh gerakan lengan prostetik bionik. Kemudian, kecepatan servo meningkat secara signifikan sebesar 74.16%. Servo dengan menggunakan DBVR berhasil mencapai posisi sudut target dengan tingkat keberhasilan rata-rata 92%, dimana lebih baik 16% dibandingkan dengan servo tanpa penerapan DBVR. Oleh karena itu, penggunaan DBVR telah terbukti efektif dalam meningkatkan kecepatan dan akurasi servo dalam mencapai posisi sudut target pada seluruh gerakan lengan prostetik bionik, meliputi gerakan buka, gerakan genggam, gerakan sip, gerakan ok-tengah, gerakan cool, gerakan spiderman, gerakan pistol dan gerakan tengah.

 

Abstract

Globally, there are 57.7 million individuals who have lost their arms and face challenges in performing daily tasks. Currently, bionic prosthetic arms have been developed, one of which is the "Recognition System for Bionic Forearm Prosthetic Hand Movements Using K-Nearest Neighbor Method Based on Electromyography Signal" (Adani, M. S., Widasari, E.R., 2023). However, this research has some limitations in terms of speed and servo angle positions. To address this issue, this study proposes the use of Decision Based Velocity Ramp (DBVR) as a post-processing method to improve the servo speed in reaching target positions and enhance the precision of target servo angle positions. The results indicate that Myoware sensor readings have a very high accuracy level, up to 100%, with a minimum value of 0.18V and a maximum of 2.84V for all bionic prosthetic arm movements. Furthermore, the servo speed increased significantly by 74.16%. Servo, when using DBVR, successfully reached the target angle positions with an average success rate of 92%, which is 16% better compared to servo without the implementation of DBVR. Therefore, the use of DBVR has proven to be effective in improving the speed and accuracy of the servo in reaching target angle positions for all bionic prosthetic arm movements, including open movement, grip movement, sip movement, ok-middle movement, cool movement, spiderman movement, pistol movement, and middle movement.

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Referensi

ADANI, M. S. N. & ROSANA, E., 2023. Sistem Pengenalan Pergerakan Prostetik. Perpustakaan J-PTIIK.

ARDUINO, 2023. Arduino Mega 2560 REV3. [Online] Available at: https://docs.arduino.cc/hardware/mega-2560 [Diakses 25 November 2023].

CAMPANINI, I., DISSELHORST-KLUG, C., RYMER, W. & MERLETTI, R., 2019. Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use. Journal of Electromyography and Kinesiology, Volume 46, pp. 70-83.

GOHEL, V. & MEHENDALE, N., 2020. Review on electromyography signal acquisition and processing. Biophysical Review, pp. 1361-1367.

GUO, G. et al., 2003. KNN model-based approach in classification.. On The Move to Meaningful Internet Systems 2003, pp. 986-996.

MCDONALD, C. et al., 2021. Global prevalence of traumatic non-fatal limb amputation. Prosthet Orthot Int, 45(2), pp. 105-114.

MEIER III, R. & MELTON, D., 2014. Ideal Functional Outcomes for Amputation Levels. Physical Medicine and Rehabilitation Clinics, 17 April, Volume 25, pp. 199-212.

MYOWARE, 2022. Muscle Sensor. [Online] Available at: https://myoware.com/wp-content/uploads/2022/03/MyoWare_v2_AdvancedGuide-Updated.pdf [Diakses 19 November 2023].

PITTARA, 2022. Amputasi. [Online]

Available at: https://www.alodokter.com/amputasi [Diakses 5 October 2023].

PRAKASH, A., SHARMA, S. & SHARMA, N., 2019. A compact sized surface EMG sensor for myoelectric hand prosthesis. Biomedical Engineering Letters, pp. 467-479.

RAHAYUNINGSIH, I., WIBAWA, A. D. & PRAMUNANTO, E., 2018. Klasifikasi Bahasa Isyarat Indonesia Berbasis Sinyal EMG Menggunakan Fitur Time Domain (MAV, RMS, VAR, SSI). JURNAL TEKNIK ITS, Volume 7, pp. 2337-3520.

SIMON, A. M. & HARGROVE, L. J., 2011. A comparison of the effects of majority vote and a decision-based. 33rd Annual International Conference of the IEEE EMBS.

SIMON, A. M., HARGROVE, L. J., LOCK, B. A. & KUIKEN, T. A., 2011. A Decision-Based Velocity Ramp for Minimizing the Effect of Misclassifications During Real-Time Pattern Recognition Control. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 58(8), pp. 2360-2368.

STANLEY, G., 2020. Exponential Filter. [Online]

Available at: https://gregstanleyandassociates.com/whitepapers/FaultDiagnosis/Filtering/Exponential-Filter/exponential-filter.htm [Diakses 13 September 2023].

TOWERPRO, 2016. MG996R Datasheet. [Online]

Available at: https://pdf1.alldatasheet.com/datasheet-pdf/view/1131873/ETC2/MG996R.html

Diterbitkan

30-06-2025

Terbitan

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Ilmu Komputer

Cara Mengutip

Pengoptimalan Gerakan Lengan Prostetik Bionik Menggunakan Decision Based Velocity Ramp. (2025). Jurnal Teknologi Informasi Dan Ilmu Komputer, 12(3), 617-624. https://doi.org/10.25126/jtiik.2025128535