Articles
| Open Access | INTEGRATING VHDL AND ARTIFICIAL NEURAL NETWORKS FOR EMG SIGNAL CLASSIFICATION
M.R. Aahsan , Department of Electrical and Computer Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, Kuala Lumpur, MalaysiaAbstract
This study explores the integration of VHDL (VHSIC Hardware Description Language) with Artificial Neural Networks (ANNs) for the classification of Electromyography (EMG) signals, aiming to enhance the performance and efficiency of real-time signal processing applications. EMG signals, which reflect electrical activity in muscles, are often used in various medical and prosthetic applications, necessitating accurate and rapid classification for effective outcomes. Traditional software-based approaches to EMG signal classification can be limited by processing speed and computational constraints, especially in real-time systems.
By leveraging VHDL, a hardware description language used for designing and modeling digital systems, this research develops a hardware-accelerated solution that integrates ANNs for EMG signal classification. The approach involves designing an ANN model tailored for EMG signal analysis and implementing this model in VHDL to create an efficient hardware architecture. This integration facilitates high-speed processing and low-latency classification, addressing the limitations of software-based methods.
The VHDL model incorporates key components of the ANN, including input layers, hidden layers, and output layers, into a hardware-efficient design. The implementation is optimized for FPGA (Field-Programmable Gate Array) platforms, allowing for real-time processing of EMG signals with improved accuracy and speed. Experimental results demonstrate that the VHDL-based ANN classification system significantly outperforms traditional software approaches in terms of processing speed and classification accuracy.
The study highlights the advantages of combining VHDL with ANNs for EMG signal classification, providing a robust solution for applications requiring real-time data analysis. This hardware-accelerated approach opens new possibilities for advanced medical devices, prosthetic control systems, and other applications where timely and precise signal classification is crucial. The research contributes to the field of digital signal processing by demonstrating an effective methodology for integrating hardware and neural network technologies.
Keywords
Artificial Neural Networks, EMG Signal Classification, Hardware Description Language
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