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Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network

Journal of Medical Systems volume 44, Article number: 176 (2020) Cite this article

Abstract

Few studies in the literature have researched the use of surface electromyography (sEMG) for motor assessment post-stroke due to the complexity of this type of signal. However, recent advances in signal processing and machine learning have provided fresh opportunities for analyzing complex, non-linear, non-stationary signals, such as sEMG. This paper presents a method for identification of the upper limb movements from sEMG signals using a combination of digital signal processing, that is discrete wavelet transform, and the enhanced probabilistic neural network (EPNN). To explore the potential of sEMG signals for monitoring motor rehabilitation progress, this study used sEMG signals from a subset of movements of the Arm Motor Ability Test (AMAT) as inputs into a movement classification algorithm. The importance of a particular frequency domain feature, that is the ratio of the mean absolute values between sub-bands, was discovered in this work. An average classification accuracy of 75.5% was achieved using the proposed approach with a maximum accuracy of 100%. The performance of the proposed method was compared with results obtained using three other classification algorithms: support vector machine (SVM), k-Nearest Neighbors (k-NN), and probabilistic neural network (PNN) in terms of sEMG movement classification. The study demonstrated the capability of using upper limb sEMG signals to identify and distinguish between functional movements used in standard upper limb motor assessments for stroke patients. The classification algorithm used in the proposed method, EPNN, outperformed SVM, k-NN, and PNN.

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Affiliations

  1. Department of Biomedical Engineering, The Ohio State University, Columbus, OH, 43210, USA

    Alexis Burns

  2. Departments of Biomedical Engineering, Biomedical Informatics, Neurology, and Neuroscience, The Ohio State University, Columbus, OH, 43210, USA

    Hojjat Adeli

  3. Physical Therapy Division, School of Health and Rehabilitation Sciences, The Ohio State University, 453 W 10th Ave, Rm. 516E, Columbus, OH, 43210, USA

    John A. Buford

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Correspondence to Hojjat Adeli.

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Appendix 1

Appendix 1

Table 4 The table below lists the 10 movements used for the AMAT as adapted by O’Dell et al. [38]. This adaptation reduced the number of movements to be performed from 13 to 10 to increase the clinical viability of the test for stroke patient movement completion. The 5 movements used in the present study are highlighted in bold font
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Burns, A., Adeli, H. & Buford, J.A. Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network. J Med Syst 44, 176 (2020). https://doi.org/10.1007/s10916-020-01639-x

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Keywords

  • Upper Limb Movement Classification
  • EMG
  • Electromyographic Signals
  • Enhanced Probablistic Neural Network
  • Surface EMG
  • Semg
  • Machine learning
  • Wavelet transform
  • Motor rehabilitation