Abstract
Fatal accidents are an inseparable part of life which often costs us loss of limbs especially hands and legs and turns any of our body asset into a burden to the family, as well as the society. The only solution to such misfortune is to facilitate the human with a new taste of living a happy life by having an artificial arm or leg that would be functional enough to have the everyday life activities smoother. The only way to make any artificial organ functional is to mimic its working from the remaining part of the organ or from the other similar organ more efficiently which is achieved by successfully and more intelligently extracting the features from biomedical signals especially from electromyogram (EMG) signals. This paper analyzes and detects various hand movements from surface EMG signals for six classes using a state-of-the-art Machine Learning scheme. This paper also presents an overview of the contemporary research on hand movement detection using EMG signals. The main contribution of this paper is the application of Convolutional Neural Network (CNN) in selection of the features from EMG signals along with some data preprocessing schemes including Fast Fourier Transform (FFT). The proposed approach has been applied to the available EMG dataset and demonstrates better accuracy along with computational simplicity than most existing schemes.
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Rajon, S.A.A. et al. (2023). Analysis of Hand Movement from Surface EMG Signals Using Artificial Neural Network. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 491. Springer, Cham. https://doi.org/10.1007/978-3-031-34622-4_6
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