AIHAND

The advancement of prosthetic technology has significantly evolved with the integration of Artificial Neural Networks (ANN) to create more intuitive and responsive prosthetic limbs. A prime example is the development of prosthetic hands that utilize Electromyography (EMG) and Electroencephalography (EEG) signals to enhance control and functionality. EMG sensors detect electrical signals generated by muscle contractions, which are then processed by an ANN model to interpret the user's intended movement. This allows the prosthetic hand to mimic natural hand motions based on the user's muscle activity, providing a more seamless and intuitive experience. On the other hand, EEG sensors capture brainwave activity, enabling the user to control the prosthetic hand with thought commands, making the system even more advanced and user-friendly. The integration of both EMG and EEG with ANN creates a closed-loop system, where the prosthetic hand learns and adapts to the user’s specific movements and intentions over time. This leads to a significant improvement in the precision and responsiveness of the prosthetic hand, allowing users to perform complex tasks that were previously difficult or impossible with traditional prosthetics. Moreover, this technology can also incorporate feedback mechanisms, such as sensory feedback, to provide users with a sense of touch or pressure, further improving the usability and comfort of the prosthetic. As a result, the combination of ANN with EMG and EEG is paving the way for next-generation prosthetics that not only restore functionality but also provide a more natural and human-like interaction between the user and their prosthetic limb. These advancements hold great potential in enhancing the quality of life for amputees, empowering them to regain greater independence and engage more fully in daily activities.