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How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code

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Abstract

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89 s for all subjects, and the execution time of a single movement was 381 ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position.

Graphical Abstract

The main characteristics of this work are as follows.

1) The sEMG signals of short-time muscle contraction and long-time muscle contraction are encoded as Morse code to realize the recognition of six movements.

2) This work only uses Morse code and multiple judgments combined with marker bits to realize action recognition. It doesn't use the sophisticated machine learning and deep learning methods. Therefore, it is simple to calculate and does not need to collect a large amount of data in advance for training the model.

3) This work does not need to collect a large amount of data from subjects in advance to train the model, so there is no consistency requirement for human individuals and human posture.

4) This method makes equal scaling and threshold determination according to the amplitude of the integrated sEMG (IEMG) signal, so it is not necessary to ensure that the pasting position of the sEMG sensor in each measurement is consistent.

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Acknowledgements

The study was funded and supported by the National Natural Science Foundation of China (Grant No. 72188101), National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (Grant No. 52025054), National Natural Science Foundation of China (Grant No. 52105017), The University Synergy Innovation Program of Anhui Province (Grant No. GXXT-2023-106), and Fundamental Research Funds for the Central Universities (Grand No. JZ2022HGTB0293).

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Authors and Affiliations

  1. School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, China

    Feiyun Xiao, Jingsong Mu, Liangguo He & Yong Wang

  2. Chizhou Huayu Electronic Technology Company Ltd., Chizhou, 247100, China

    Feiyun Xiao

  3. Anhui Province Key Laboratory of Digital Design and Manufacturing, Hefei, China

    Feiyun Xiao

  4. Department of Rehabilitation Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Anhui Provincial Hospital, Hefei, 230001, People’s Republic of China

    Jingsong Mu

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  1. Feiyun Xiao
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Correspondence to Feiyun Xiao.

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Xiao, F., Mu, J., He, L. et al. How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03109-9

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