A parallel classification strategy to simultaneous control elbow, wrist, and hand movements.
Background: In the field of myoelectric control systems, pattern recognition (PR) algorithms have become always more interesting for predicting complex electromyography patterns involving movements with more than 2 Degrees of Freedom (DoFs). The majority of classification strategies, used for the pr...
| Publicado en: | Journal of NeuroEngineering & Rehabilitation (JNER) Vol. 19; no. 1; pp. 1 - 18 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
1/28/2022
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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=154979469&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154979469 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17430003 1CUC jtl: Journal of NeuroEngineering & Rehabilitation (JNER) issn: 17430003 maglogo: N pubinfo: dt: 1/28/2022 vid: 19 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 154979469 154979469 NLM35090512 154979469 10.1186/s12984-022-00982-z NLM35090512 154979469 ppf: 1 ppct: 17 formats: tig: atl: A parallel classification strategy to simultaneous control elbow, wrist, and hand movements. aug: au: Leone, Francesca Gentile, Cosimo Cordella, Francesca Gruppioni, Emanuele Guglielmelli, Eugenio Zollo, Loredana affil: Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, Rome, Italy sug: subj: Wrist Limb Prosthesis Movement Electromyography Methods Wrist Joint Hand Information Science Methods Elbow Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: Background: In the field of myoelectric control systems, pattern recognition (PR) algorithms have become always more interesting for predicting complex electromyography patterns involving movements with more than 2 Degrees of Freedom (DoFs). The majority of classification strategies, used for the prosthetic control, are based on single, hierarchical and parallel linear discriminant analysis (LDA) classifiers able to discriminate up to 19 wrist/hand gestures (in the 3-DoFs case), considering both combined and discrete motions. However, these strategies were introduced to simultaneously classify only 2 DoFs and their use is limited by the lack of online performance measures. This study introduces a novel classification strategy based on the Logistic Regression (LR) algorithm with regularization parameter to provide simultaneous classification of 3 DoFs motion classes.Methods: The parallel PR-based strategy was tested on 15 healthy subjects, by using only six surface EMG sensors. Twenty-seven discrete and complex elbow, hand and wrist motions were classified by keeping the number of electromyographic (EMG) electrodes to a bare minimum and the classification error rate under 10 %. To this purpose, the parallel classification strategy was implemented by using three classifiers one for each DoF: the "Elbow classifier", the "Wrist classifier", and the "Hand classifier" provided the simultaneous control of the elbow, hand, and wrist joints, respectively.Results: Both the offline and real-time performance metrics were evaluated and compared with the LDA parallel classification results. The real-time recognition results were statistically better with the LR classifier with respect to the LDA classifier, for all motion classes (elbow, hand and wrist).Conclusions: In this paper, a novel parallel PR-based strategy was proposed for classifying up to 3 DoFs: three joint classifiers were employed simultaneously for classifying 27 motion classes related to the elbow, wrist, and hand and promising results were obtained. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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