<p> The proposed EMG data acquisition band, which utilizes seven electrodes, has the potential to revolutionize gesture recognition. Unlike conventional EMG data acquisition methods that necessitate skin preparation and can cause skin irritation or adverse reactions, this band is inexpensive, user-friendly, and capable of capturing high-quality EMG signals. Our study evaluated the performance of the proposed EMG band by comparing it with established techniques on eight healthy subjects, and the results revealed that it achieved comparable or significantly better performance in terms of classification accuracy. This suggests that our EMG data acquisition band has significant practical applications, particularly in prosthetic hand control, where real-time processing is critical. Moreover, The proposed feature extraction method based on spectral moments also exhibited significant improvements in terms of classification accuracy, class separability, and processing time, making it a promising alternative for gesture recognition applications. Additionally, the proposed feature set was computationally efficient, with a maximum computation time of 250.209 milliseconds, making it suitable for real-time prosthetic hand control. </p>

<p> The proposed EMG data acquisition band, which utilizes seven electrodes, has the potential to revolutionize gesture recognition. Unlike conventional EMG data acquisition methods that necessitate skin preparation and can cause skin irritation or adverse reactions, this band is inexpensive, user-friendly, and capable of capturing high-quality EMG signals. Our study evaluated the performance of the proposed EMG band by comparing it with established techniques on eight healthy subjects, and the results revealed that it achieved comparable or significantly better performance in terms of classification accuracy. This suggests that our EMG data acquisition band has significant practical applications, particularly in prosthetic hand control, where real-time processing is critical. Moreover, The proposed feature extraction method based on spectral moments also exhibited significant improvements in terms of classification accuracy, class separability, and processing time, making it a promising alternative for gesture recognition applications. Additionally, the proposed feature set was computationally efficient, with a maximum computation time of 250.209 milliseconds, making it suitable for real-time prosthetic hand control. </p>