Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network
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IMAGE & SIGNAL PROCESSING
Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network Alexis Burns 1 & Hojjat Adeli 2
&
John A. Buford 3
Received: 17 April 2019 / Accepted: 5 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Upper Limb Movement Classification . EMG . Electromyographic Signals . Enhanced Probablistic Neural Network . Surface EMG . Semg . Machine learning . Wavelet transform . Motor rehabilitation
Introduction Surface electromyographic (sEMG) signals are electrical potentials in the muscles during movement that are acquired by
This article is part of the Topical Collection on Image & Signal Processing * Hojjat Adeli [email protected] Alexis Burns [email protected] 1
Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
2
Departments of Biomedical Engineering, Biomedical Informatics, Neurology, and Neuroscience, The Ohio State University, Columbus, OH 43210, USA
3
Physical Therapy Division, School of Health and Rehabilitation Sciences, The Ohio State University, 453 W 10th Ave, Rm. 516E, Columbus, OH 43210, USA
electrodes applied on the surface of the skin [8, 16]. These signals are generated by the brain and transmitted and optimized through neural pathways and networks to produce the appropriate muscle activation patterns for both voluntary and involuntary movem
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