Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

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aut.relation.endpage15
aut.relation.issue5
aut.relation.journalSensors
aut.relation.startpage1
aut.relation.volume21
dc.contributor.authorNoor, A
dc.contributor.authorWaris, A
dc.contributor.authorGilani, SO
dc.contributor.authorKashif, AS
dc.contributor.authorJochumsen, M
dc.contributor.authorIqbal, J
dc.contributor.authorNiazi, IK
dc.date.accessioned2023-06-13T04:28:05Z
dc.date.available2023-06-13T04:28:05Z
dc.date.issued2021-02-24
dc.description.abstractStroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed oth-ers (p < 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients.
dc.identifier.citationSensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 21(5), 1-15. doi: 10.3390/s21051575
dc.identifier.doi10.3390/s21051575
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/10292/16262
dc.languageeng
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/1424-8220/21/5/1575
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectankle joint movements
dc.subjecthome-based physical therapy
dc.subjectlower limb functional recovery
dc.subjectpattern recognition (PR)
dc.subjectstroke rehabilitation
dc.subjectsurface electromyography (sEMG)
dc.subject40 Engineering
dc.subject4008 Electrical Engineering
dc.subject4009 Electronics, Sensors and Digital Hardware
dc.subjectAging
dc.subjectClinical Research
dc.subjectRehabilitation
dc.subjectNeurosciences
dc.subjectBrain Disorders
dc.subjectStroke
dc.subjectPhysical Rehabilitation
dc.subjectStroke
dc.subject0301 Analytical Chemistry
dc.subject0502 Environmental Science and Management
dc.subject0602 Ecology
dc.subject0805 Distributed Computing
dc.subject0906 Electrical and Electronic Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.subject.meshAnkle Joint
dc.subject.meshElectromyography
dc.subject.meshHumans
dc.subject.meshMovement
dc.subject.meshStroke
dc.subject.meshStroke Rehabilitation
dc.subject.meshAnkle Joint
dc.subject.meshHumans
dc.subject.meshElectromyography
dc.subject.meshMovement
dc.subject.meshStroke
dc.subject.meshStroke Rehabilitation
dc.subject.meshAnkle Joint
dc.subject.meshElectromyography
dc.subject.meshHumans
dc.subject.meshMovement
dc.subject.meshStroke
dc.subject.meshStroke Rehabilitation
dc.titleDecoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography
dc.typeJournal Article
pubs.elements-id508409
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