Evaluation of surface EMG-based recognition algorithms for decoding hand movements
Evaluation of surface EMG-based recognition algorithms for decoding hand movements
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DOI：
10.1007/s11517-019-02073-z
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发表时间：
2019-11-21
影响因子：
3.2
通讯作者：
Ortiz-Catalan, Max
中科院分区：
文献类型：
--
作者：
Abbaspour, Sara;Linden, Maria;Ortiz-Catalan, Max
肌电模式识别（MPR）解码肢体运动是动力假肢控制的重要进展。然而，这项技术尚未广泛应用于临床。MPR的改进可能会潜在地增加动力假肢的功能。为此，使用6个分类器测量了44个特征的离线精度和处理时间，目的是确定特征和分类器的新配置，以提高假肢控制的精度和响应时间。找到了一种有效的特征集（FS：波形长度、相关系数、Hjorth参数）来提高运动识别的精度。与Hudgins集相比，使用本文提出的FS可显著提高线性判别分析、k近邻、最大似然估计（MLE）和支持向量机的性能，分别提高5.5%、5.7%、6.3%和6.2%。与Hudgins特征集相比，使用FS和MLE在离线精度方面提供了最大的改进，而对处理时间的影响最小。在测试的44个特征中，对数均方根和归一化对数能量的识别率最高（95%以上）。我们预计这项工作将有助于开发更精确的基于表面肌电图的运动解码系统，用于控制假手。
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.