Developing a Low-Cost Prosthetic Hand with Real-Time Machine Learning Capabilities

The global prevalence of arm amputations, particularly transradial or below-elbow amputations, presents a pressing challenge due to the high costs of commercially available prosthetic hands. This issue is particularly acute in developing countries, where financial constraints hinder access to functional prosthetic solutions. Addressing this gap, this research introduces an innovative approach that merges electromyography (EMG) signal recognition and machine learning to develop an affordable and versatile bionic below-elbow prosthetic hand. The integration of EMG signals for control, coupled with machine learning algorithms, enables accurate classification of hand movements, resulting in enhanced movement precision and a broader range of gestures. This solution overcomes limitations in existing prosthetic hands, offering advanced EMG-based control and comprehensive movement capabilities, empowering users to perform intricate tasks and regain functional autonomy. The study undertakes a comprehensive analysis of the developed system's performance, encompassing hardware, software, and machine learning components. Results show promising accuracy levels for movement classification using the K-Nearest Neighbors (KNN) machine learning method. Execution time variations are observed among subjects and gestures, influenced by factors such as subject sensitivity and gesture complexity. Energy consumption analysis reveals disparities in power usage among different movements, emphasizing the impact of movement complexity on power requirements. A cost analysis demonstrates the affordability of the prototype, positioning it as a cost-effective alternative to existing prosthetic hand options. Comparing this solution to other research and commercial prosthetic hands highlights its uniqueness, particularly its emphasis on affordability.