Towards reducing the impacts of unwanted movements on identification of motion intentions.
Surface electromyogram (sEMG) has been extensively used as a control signal in prosthesis devices. However, it is still a great challenge to make multifunctional myoelectric prostheses clinically available due to a number of critical issues associated with existing EMG based control strategy. One su...
| Publicado en: | Journal of Electromyography & Kinesiology Vol. 28; pp. 90 - 99 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Jun2016
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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=115368423&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115368423 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10506411 JIX jtl: Journal of Electromyography & Kinesiology issn: 10506411 maglogo: N pubinfo: dt: Jun2016 vid: 28 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 115368423 115368423 NLM27093136 10.1016/j.jelekin.2016.03.005 NLM27093136 115368423 ppf: 90 ppct: 9 formats: tig: atl: Towards reducing the impacts of unwanted movements on identification of motion intentions. aug: au: Li, Xiangxin Chen, Shixiong Zhang, Haoshi Samuel, Oluwarotimi Williams Wang, Hui Fang, Peng Zhang, Xiufeng Li, Guanglin affil: Key Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences (CAS), Shenzhen, Guangdong 518055, China sug: subj: Algorithms Amputees Muscle, Skeletal Physiology Movement Limb Prosthesis Adverse Effects Intention Limb Prosthesis Standards Information Science Methods Female Case Control Studies Electromyography Methods Adult Male Impact of Events Scale Scales Adult: 19-44 years Female Male ab: Surface electromyogram (sEMG) has been extensively used as a control signal in prosthesis devices. However, it is still a great challenge to make multifunctional myoelectric prostheses clinically available due to a number of critical issues associated with existing EMG based control strategy. One such issue would be the effect of unwanted movements (UMs) that are inadvertently done by users on the performance of movement classification in EMG pattern recognition based algorithms. Since UMs are not considered in training a classifier, they would decay the performance of a trained classifier in identifying the target movements (TMs), which would cause some undesired actions in control of multifunctional prostheses. In this study, the impact of UMs was systemically investigated in both able-bodied subjects and transradial amputees. Our results showed that the UMs would be unevenly classified into all classes of the TMs. To reduce the impact of the UMs on the performance of a classifier, a new training strategy that would categorize all possible UMs into a new movement class was proposed and a metric called Reject Ratio that is a measure of how many UMs is rejected by a trained classifier was adopted. The results showed that the average Reject Ratio across all the participants was greater than 91%, meanwhile the average classification accuracy of TMs was above 99% when UMs occurred. This suggests that the proposed training strategy could greatly reduce the impact of UMs on the performance of the trained classifier in identifying the TMs and may enhance the robustness of myoelectric control in clinical applications. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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