Low-Cost EMG Based Bionic ARM using Servo Motor and Arduino

Author:

,Shanofer J ,Sree K M Reyana, ,Avanthika C M , ,Nasith A Ahamed, ,Kiruthika K ORCID,

Abstract

Robotics is one of the technological advancement in the field of both medical and engineering. The main idea of this project is to change the standpoint of remote controlled robotic arms to manually controlled bionic arms. The primary goal of his project is to create a rehabilitation device that can help an upper arm amputee to attain their day-to-day tasks. This project is based on the design and development of the 3D printed arm structure. The whole system is the association of electrodes controlled by the EMG sensor that is attached to the 3D printed arm. This robotic bionic arm is developed using Arduino, EMG sensor, servo motor and MLX temperature sensor. The tension that are generated by the muscle contraction and relaxation results in the voltage level variation. On the bases of the different values obtained, the threshold value is sampled and the bionic arms fingers close, open, pick and place the objects according to the muscle movements. This technology not only used as prosthesis by the upper arm amputee patients, but also has its many favorable applications in the field of surgical operations like doctor operating a patient with the help of robotic arms rather than his own hands, humanoid robotics, etc.

Publisher

Lattice Science Publication (LSP)

Reference15 articles.

1. 1. Kim, H. J., Kim, J. H., Kim, S. H., & Lee, J. H. (2019). A novel myoelectric control algorithm for upper limb prosthesis using machine learning. Transactions on Neural Systems and Rehabilitation Engineering, 27(10), 1448-1456.

2. Mergel, K., & Dorn, H.-P. (2018). Towards intuitive control of multi-degree-of-freedom prosthetic arms using brain-computer interfaces. Journal of Neuro Engineering and Rehabilitation, 15(1), 1-14.

3. Kim, S., & Choi, Y. (2019). A review on myoelectric signal processing for prosthetic control. IET Science, Measurement & Technology, 13(4), 609-619.

4. Kim, S. H., & Choi, Y. (2019). A review on myoelectric signal processing for prosthetic control. IET Science, Measurement & Technology, 13(4), 609-619.

5. Chang, Y. C., Chang, C. C., & Chen, C. Y. (2017). A novel myoelectric control algorithm for upper limb prosthesis using machine learning. Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1539-1548.

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