Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network.
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-line...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 10 |
|---|---|
| Autores principales: | , , |
| Formato: | pictorial research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
Oct2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146224559&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146224559 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2020 vid: 44 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146224559 146224559 146224559 10.1007/s10916-020-01639-x 146224559 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network. aug: au: Burns, Alexis Adeli, Hojjat Buford, John A. affil: Department of Biomedical Engineering, The Ohio State University, 43210, Columbus, OH, USA sug: subj: Upper Extremity Physiology Electromyography Neural Networks (Computer) Signal Processing, Computer Assisted Human Clinical Assessment Tools Algorithms Descriptive Statistics Stroke Patients Machine Learning Biceps Brachii Muscles Physiology Confidence Intervals Stroke Rehabilitation Movement ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|