FORS-EMG: A Novel sEMG Dataset for Hand Gesture Recognition Across Multiple Forearm Orientations

• URL: http://arxiv.org/abs/2409.07484v1
• Date: Tue, 3 Sep 2024 14:23:06 GMT
• Title: FORS-EMG: A Novel sEMG Dataset for Hand Gesture Recognition Across Multiple Forearm Orientations
• Authors: Umme Rumman, Arifa Ferdousi, Md. Sazzad Hossain, Md. Johirul Islam, Shamim Ahmad, Mamun Bin Ibne Reaz, Md. Rezaul Islam,
• Abstract summary: Surface electromy (sEMG) signal holds great potential in the research fields of gesture recognition and the development of robust prosthetic hands. The sEMG signal is compromised with physiological or dynamic factors such as forearm orientations, forearm displacement, limb position, etc. In this paper, we have proposed a dataset of electrode sEMG signals to evaluate common daily living hand gestures performed with three forearm orientations.
• Score: 1.444899524297657
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Surface electromyography (sEMG) signal holds great potential in the research fields of gesture recognition and the development of robust prosthetic hands. However, the sEMG signal is compromised with physiological or dynamic factors such as forearm orientations, electrode displacement, limb position, etc. The existing dataset of sEMG is limited as they often ignore these dynamic factors during recording. In this paper, we have proposed a dataset of multichannel sEMG signals to evaluate common daily living hand gestures performed with three forearm orientations. The dataset is collected from nineteen intact-limed subjects, performing twelve hand gestures with three forearm orientations: supination, rest, and pronation.Additionally, two electrode placement positions (elbow and forearm) are considered while recording the sEMG signal. The dataset is open for public access in MATLAB file format. The key purpose of the dataset is to offer an extensive resource for developing a robust machine learning classification algorithm and hand gesture recognition applications. We validated the high quality of the dataset by assessing the signal quality matrices and classification performance, utilizing popular machine learning algorithms, various feature extraction methods, and variable window size. The obtained result highlighted the significant potential of this novel sEMG dataset that can be used as a benchmark for developing hand gesture recognition systems, conducting clinical research on sEMG, and developing human-computer interaction applications. Dataset:https://www.kaggle.com/datasets/ummerummanchaity/fors-emg-a-novel-semg-dataset/data

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