Exploring EMG Signals for Prosthetic Control and Gesture Recognition: Advancements, Applications, and Future Directions

EMG signals and machine learning enable the automation of gesture recognition for addressing challenges faced by amputees in daily life. This review analyses diverse approaches within EMG-based gesture recognition and hand prosthesis control. Shifting muscular intention into natural control offers numerous applications which include helping amputees regain their functioning to completely changing the way we interact with digital surroundings. It highlights advancements in this dynamic field, emphasizing its applications in human-computer interaction, prosthetics and assistive communication. The review focuses on techniques that improve accuracy and robustness which are essential for wider accessibility. Additionally, it explores the potential impact on industries beyond healthcare such as AR/VR, prosthetic hand control, human-machine interaction and sign language recognition. The performance of hybrid approaches and convolutional neural networks (CNNs) in gesture recognition tests demonstrates a high degree of accuracy. Overall, EMG-based gesture recognition offers significant promise in driving innovations that enhance quality of life and promote inclusivity. This exploration delves into the recent strides, diverse applications, and future directions of EMG-based prosthetic control and gesture recognition, offering insights into the groundbreaking research shaping this domain.