Classification of simultaneous movements using surface EMG pattern recognition.
Classification of simultaneous movements using surface EMG pattern recognition.
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DOI：
10.1109/tbme.2012.2232293
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发表时间：
2013-05
期刊：
影响因子：
--
通讯作者：
Hargrove LJ
中科院分区：
文献类型：
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作者：
Young AJ;Smith LH;Rouse EJ;Hargrove LJ
先进的上肢假肢能够驱动多个自由度（DOF），现在可以在商业上使用。使用表面肌电信号的模式识别算法作为多自由度控制器显示出巨大的前景。不幸的是，当前的模式识别系统一次只能激活一个自由度。本文介绍了一种基于贝叶斯理论的分类器，对同步运动进行分类。该方法和另外两种同时运动的分类策略被评估，使用非截肢者和截肢者受试者对多达三个自由度进行分类，其中任意两个自由度可以同时分类。在非截肢者和截肢者中也发现了类似的结果。基于一组条件并行分类器的新方法最有希望，其误差显著小于单个LDA分类器或并行方法（p<0.05）。对于3自由度分类，条件并行方法在离散运动和组合运动上的错误率分别为6.6%和10.9%，而单个LDA在离散运动和组合运动上的错误率分别为9.4%和14.1%。低错误率表明，表面肌电模式识别技术可以扩展到识别同步运动，这可以为截肢者提供更逼真的运动，而不是仅仅分类顺序运动。
Advanced upper-limb prostheses capable of actuating multiple degrees of freedom (DOF) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one degree of freedom at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using non-amputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for non-amputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less (p<0.05) than a single LDA classifier or a parallel approach. For 3-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.