Prosthetic hand control using myo-armband and machine learning for gesture recognition

This project focuses on a myoelectrically controlled prosthetic hand that uses machine learning and a Myo armband for gesture recognition. The Myo armband captures muscle activity and translates it into control signals, allowing the prosthetic hand to perform various gestures. Machine learning algorithms are employed to accurately recognize and differentiate between these gestures, enabling intuitive and responsive control. The integration of these technologies results in a prosthetic hand that can seamlessly adapt to the user's intended movements, offering enhanced functionality and ease of use.