Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography
Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography
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DOI：
10.1109/jbhi.2013.2249590
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发表时间：
2013-05-01
影响因子：
7.7
通讯作者：
Outram, Nicholas
中科院分区：
文献类型：
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作者：
Al-Timemy, Ali H.;Bugmann, Guido;Outram, Nicholas
提出了一种用于对假手灵巧控制的手指运动分类的方法。之前的研究主要致力于识别手部动作，因为这些动作会产生从前臂记录的强烈肌电图（EMG）信号。相比之下，在本文中，我们评估了使用多通道表面肌电图 (sEMG) 对单独和组合的手指运动进行分类，以实现灵巧的假肢控制。记录了 10 名肢体完整和 6 名肘部以下截肢者的 sEMG 通道。使用离线处理来评估分类性能。结果表明，通过时域自回归特征提取、用于特征缩减的正交模糊邻域判别分析和用于分类的线性判别分析组成的处理链可以实现高分类精度。我们证明，手指和拇指的运动可以被准确地解码，且延迟短至 200 毫秒。首次对六名截肢者的拇指外展进行高精度解码。我们还发现，六个 EMG 通道的子集提供的准确度值与使用全套 EMG 通道计算的准确度值类似（对 10 个肢体完整的受试者对 15 类不同手指运动进行分类的准确度为 98%，对 6 个截肢者对 12 类个人手指运动的分类准确度为 90%）。这些准确度值高于以前的研究，而我们通常在每个识别的运动中使用一半数量的肌电图通道。
A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement.