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Efficient deep neural network model for classification of grasp types using sEMG signals

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Abstract

Grasping is a challenging problem in robotics and prosthetic applications due to its control requirements. The visual perception and analyzing electromyography (EMG) signals are the two ways to give the inputs to robots and prosthetic amputees for grasping abilities. The EMG is a diagnostic manner that evaluates the fitness condition of skeletal muscles. Examination or evaluation of the EMG signals is time-consuming and arduous for experts. Hence, the state-of-the-art methods in artificial intelligence (AI) is employed to improve the accuracy rate for the detection and classification of EMG signals for grasping. Recently, deep learning architectures have been used in many engineering applications such as diagnosis of health conditions, computer vision, and human machine interaction (HMI). In this study, a new deep one-dimensional convolutional neural network model (1D-CNN) is proposed to classify six types of hand movements. Our proposed 1D-CNN model implemented using surface EMG (sEMG) has obtained the highest accuracy of 94.94% in classifying six hand movements. The strength of our model is that, it can perform the automated classification of various hard grasps using only one channel data. Our developed prototype model is ready to be tested with more data and can be used to assist in musculoskeletal disorders.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Bingol University, Bingol, Turkey

    Musab Coskun

  2. Department of Software Engineering, Firat University, Elazig, Turkey

    Ozal Yildirim

  3. Department of Electrical and Electronics Engineering, Firat University, Elazig, Turkey

    Yakup Demir

  4. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  5. Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan

    U. Rajendra Acharya

  6. International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan

    U. Rajendra Acharya

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  1. Musab Coskun
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  2. Ozal Yildirim
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  3. Yakup Demir
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  4. U. Rajendra Acharya
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Correspondence to Musab Coskun or U. Rajendra Acharya.

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Coskun, M., Yildirim, O., Demir, Y. et al. Efficient deep neural network model for classification of grasp types using sEMG signals. J Ambient Intell Human Comput 13, 4437–4450 (2022). https://doi.org/10.1007/s12652-021-03284-9

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