FORS-EMG: A Novel SEMG Dataset

Surface electromyography (sEMG) signals are highly valuable in research areas like gesture recognition and the development of advanced prosthetic hands. However, sEMG signals are often affected by physiological and dynamic factors such as forearm orientation, electrode displacement, and limb position. Many existing sEMG datasets overlook these dynamic factors during data collection, limiting their utility. This dataset addresses those gaps by including sEMG data from nineteen able-bodied subjects performing twelve different finger and wrist gestures across three forearm orientations: supination, rest, and pronation. Additionally, recordings were made from two electrode placement positions (near the elbow and middle of the forearm). The dataset is publicly available in MATLAB file format.