Published Paper Details:

Prosthetic technology has undergone a significant transformation with the advent of intelligent systems that integrate bio-signal processing, machine learning, and real-time control mechanisms. This review paper presents an in-depth exploration of a Smart Bionic Hand system that combines low-cost hardware components such as servo motors, Raspberry Pi, Arduino microcontrollers, and various sensors (EMG, flex, and gyroscopic sensors) with artificial intelligence algorithms to enable intuitive, adaptive, and affordable prosthetic solutions. The system captures electromyographic (EMG) signals from the user's muscles, interprets them using AI models, and actuates the mechanical hand to mimic natural human gestures. A feedback loop ensures real-time response and system learning, offering a high level of customization and comfort for the user. This approach addresses the shortcomings of traditional prosthetics, including high cost, lack of feedback, and poor adaptability. The review discusses system architecture, literature background, implementation details, and analytical performance of the Smart Bionic Hand in real-world scenarios, alongside future improvements.