Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion.
Purpose: Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation.Method...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 22; no. 1; pp. 1 - 12 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
3/18/2022
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| 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=156496527&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156496527 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 3/18/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 156496527 156496527 NLM35303877 10.1186/s12911-022-01808-7 NLM35303877 156496527 ppf: 1 ppct: 11 formats: tig: atl: Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion. aug: au: Li, Dujuan Chen, Caixia affil: North Sichuan Medical College, 631000, Nanchong, China sug: subj: Electrocardiography Fatigue Diagnosis Algorithms Electromyography Methods Exercise of Self-Care Agency Scale ab: Purpose: Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation.Methods: Twenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared.Results: IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved.Conclusion: The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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