Abstract：
BACKGROUND:For high usability, myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set in the performance of several pattern recognition algorithms during dynamic contractions. METHODS:A 9 class experiment was designed involving both static and dynamic situations. The performance of various feature extraction methods and classifiers was evaluated in terms of classification accuracy. RESULTS:It is shown that, combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static conditions provide relatively high classification accuracy also on dynamic situations. Moreover, the performance of the pattern recognition algorithms tested significantly improved by optimizing the choice of the training set. Finally, the results also showed that rather simple approaches for classification of time domain features provide results comparable to more complex classification methods of wavelet features. CONCLUSIONS:Non-stationary surface EMG signals recorded during dynamic contractions can be accurately classified for the control of multi-function prostheses.
journal_name
J Neuroeng Rehabiljournal_title
Journal of neuroengineering and rehabilitationauthors
Lorrain T,Jiang N,Farina Ddoi
10.1186/1743-0003-8-25subject
Has Abstractpub_date
2011-05-09 00:00:00pages
25issn
1743-0003pii
1743-0003-8-25journal_volume
8pub_type
杂志文章abstract：BACKGROUND:Motivation is an important factor in rehabilitation and frequently used as a determinant of rehabilitation outcome. Several factors can influence patient motivation and so improve exercise adherence. This paper presents the design of two robot devices for use in the rehabilitation of upper limb movements, th...
journal_title：Journal of neuroengineering and rehabilitation
pub_type： 临床试验,杂志文章
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journal_title：Journal of neuroengineering and rehabilitation
pub_type： 杂志文章
doi：10.1186/1743-0003-11-113
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journal_title：Journal of neuroengineering and rehabilitation
pub_type： 临床试验,杂志文章
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journal_title：Journal of neuroengineering and rehabilitation
pub_type： 杂志文章
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更新日期：2017-12-06 00:00:00
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journal_title：Journal of neuroengineering and rehabilitation
pub_type： 杂志文章,随机对照试验
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journal_title：Journal of neuroengineering and rehabilitation
pub_type： 杂志文章,随机对照试验
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journal_title：Journal of neuroengineering and rehabilitation
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journal_title：Journal of neuroengineering and rehabilitation
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journal_title：Journal of neuroengineering and rehabilitation
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journal_title：Journal of neuroengineering and rehabilitation
pub_type： 杂志文章,评审
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journal_title：Journal of neuroengineering and rehabilitation
pub_type： 杂志文章,评审
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更新日期：2018-06-20 00:00:00
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journal_title：Journal of neuroengineering and rehabilitation
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journal_title：Journal of neuroengineering and rehabilitation
pub_type： 杂志文章
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journal_title：Journal of neuroengineering and rehabilitation
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pub_type： 杂志文章,随机对照试验
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更新日期：2019-06-07 00:00:00
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