Analysis of Electromyography Signals for Control of Mechanical Prosthesis using Machine Learning Techniques

This research aims to design, develop, and evaluate a low-cost mechanical prosthesis controlled through EMG signals and integrated with IoT capabilities. The study seeks to achieve a balance between affordability and advanced functionality, making the technology accessible to a broader range of users. Additionally, the study aims to assess the usability, comfort, and overall performance of the developed prosthesis in real-world scenarios. The analysis of electromyography (EMG) signals for the control of mechanical prostheses using machine learning techniques serves the purpose of enabling individuals with limb loss to achieve more natural and intuitive control over their prosthetic devices. The ultimate goal is to enhance the quality of life for prosthesis users by providing them with efficient, responsive, and personalized control mechanisms. The successful implementation of a low-cost mechanical prosthesis controlled by EMG and IoT holds the potential to significantly improve the quality of life for individuals with limb disabilities. By combining cutting-edge technology with a focus on affordability, this research strives to contribute to the ongoing efforts to make advanced prosthetic solutions more inclusive and accessible globally.