Ahmed, A., Raheema, M., Hussein, J. (2025). Design and Implementation of Upper Limb Controlled by Human Voice and Artificial Intelligence. , 18(3), 320-326. doi: 10.30772/qjes.2024.149892.1241
Ahmed Mohee Aldeen Ahmed; Mithaq Nama Raheema; Jabbar Salman Hussein. "Design and Implementation of Upper Limb Controlled by Human Voice and Artificial Intelligence". , 18, 3, 2025, 320-326. doi: 10.30772/qjes.2024.149892.1241
Ahmed, A., Raheema, M., Hussein, J. (2025). 'Design and Implementation of Upper Limb Controlled by Human Voice and Artificial Intelligence', , 18(3), pp. 320-326. doi: 10.30772/qjes.2024.149892.1241
Ahmed, A., Raheema, M., Hussein, J. Design and Implementation of Upper Limb Controlled by Human Voice and Artificial Intelligence. , 2025; 18(3): 320-326. doi: 10.30772/qjes.2024.149892.1241

Design and Implementation of Upper Limb Controlled by Human Voice and Artificial Intelligence

Al-Qadisiyah Journal for Engineering Sciences
Volume 18, Issue 3, September 2025, Pages 320-326    PDF (7.27 M)
Document Type: Research Paper
DOI: 10.30772/qjes.2024.149892.1241
Authors
Ahmed Mohee Aldeen Ahmed* 1; Mithaq Nama Raheema2; Jabbar Salman Hussein2
1College of Engineering, Department of Electrical and Electronic Engineering, University of Kerbala, Kerbala, Iraq.
2College of Engineering, Department of Prosthetics and Orthotics Engineering, University of Kerbala, Kerbala, Iraq.
Abstract
Given the restrictions and complexity of EMG-based hand prostheses, the present work chose to investigate the possibility of a much simpler method based on voice-operated. Voice commands are simpler to understand than EMG signals to regulate the prosthetic arm. Therefore, the first and most important benefit of this method is that the related prosthesis is simple to use. Speech recognition is a vital focus in artificial intelligence, serving as a prominent mode of human interaction. Researchers have developed speech-driven prosthetic hand systems using traditional speech recognition frameworks and neural network models. This work intends to employ the Raspberry Pi 4 Model B – 4GB RAM embedded inside the prosthetic limb instead of Arduino without requiring a computer to lower hand weight; this offers simplicity of usage. The proposed system captures and transforms speech input into features resembling spectrograms. It processes them using the MLFFNN to categorize speech as signals (words) and forward it to the prosthetic hand, fingers, and wrist control system involving six servo motors. 3D printers created a light and sturdy prosthetic locally. This work stands out for the freedom of usage amputees of
this limb have, with the ability of the researcher to increase the movements by adding more signals (words). The entire system is implemented in Python using Keras and a deep learning framework with a TensorFlow backend. The simulation results demonstrate an accuracy of 99.4%, real-time test accuracy of 96.05%, and operational validation efficiency of 97.6%. These findings indicate that the MLFFNN can be effectively utilized for real-time control of prosthetic hands.
Keywords
Automatic Speech Recognition; Multi-Layer Feed Forward Neural Network; Mel-frequency cepstral coefficients; Root Mean Square; Standard Deviation
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