Evaluation of surface EMG-based recognition algorithms for decoding hand movements.
Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, o...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 1; pp. 83 - 101 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2020
|
| 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=141101364&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141101364 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2020 vid: 58 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141101364 141101364 NLM31754982 10.1007/s11517-019-02073-z NLM31754982 141101364 ppf: 83 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Evaluation of surface EMG-based recognition algorithms for decoding hand movements. aug: au: Abbaspour, Sara Lindén, Maria Gholamhosseini, Hamid Naber, Autumn Ortiz-Catalan, Max affil: School of Innovation, Design and Engineering, Mälardalen University, 721 23, Västerås, Sweden sug: subj: Movement Physiology Hand Physiology Electromyography Algorithms Time Factors Young Adult Adult Signal Processing, Computer Assisted Factor Analysis Middle Age Scales Adult: 19-44 years Middle Aged: 45-64 years ab: Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins' set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|