An EMG Controlled Bionic Arm with Machine Learning - Uygun, M.I.; Recber, B.; Celikli, M.A.; Oniz, Y. (Institute of Electrical and Electronics Engineers Inc., 2023)
This study uses electromyography (EMG) signals and machine learning techniques to create a bionic arm specifically made for amputees. The user's intended movements can be decoded and converted into orders for the bionic arm by observing and analyzing these signals. An EMG sensor was initially placed on the surface of particular muscles in the lower arm as part of the project's data-collection phase. As the muscles performed various movements, the electrodes captured the bioelectric signals they produced. These recorded bioelectrical signals are classified according to each movement by going through a series of processes, and it was aimed to increase the functionality of the bionic arm by gaining many movements control. The results showed promising advancements in the field and highlighted the potential for improving the quality of living for people with limb loss. © 2023 IEEE.
7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 -- 26 October 2023 through 28 October 2023 -- -- 194332
Keywords: Bioelectrical Signals; Bionic; Electromyography (Emg); Limb Loss; Machine Learning; Mechanical Arm Design; Prosthetics; Bionics; Machine Learning; Muscle; Bioelectrical Signals; Electromyography; Electromyography Signals; Limb Loss; Lower Arm; Machine Learning Techniques; Machine-Learning; Mechanical Arm; Mechanical Arm Design; Project Data; Artificial Limbs