Real-Time Locomotion Mode Recognition Employing Correlation Feature Analysis Using EMG Pattern
Real-Time Locomotion Mode Recognition Employing Correlation Feature Analysis Using EMG Pattern
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DOI：
10.4218/etrij.13.0113.0064
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发表时间：
2014-02-01
期刊：
影响因子：
1.4
通讯作者：
Ryu, Jaehwan
中科院分区：
文献类型：
--
作者：
Kim, Deok-Hwan;Cho, Chi-Young;Ryu, Jaehwan
本文提出了一种新的运动模式识别方法的基础上转换的相关特征分析，使用肌电图（EMG）模式。每个运动被识别使用六个加权子相关滤波器，这是通过使用六个时域特征应用于相关特征分析。所提出的方法具有较高的识别率，因为它反映了不同的功能，根据运动的重要性，从而使一个识别实时肌电信号模式，由于快速执行的相关特征分析。实验结果表明，对于给定的正常运动数据，该方法在水平面上行走时的识别率为85.89%（+/- 2.5），在上楼梯时的识别率为96.47%（+/- 0.9），在下楼梯时的识别率为96.37%（+/- 1.3）。这使得它的准确性和稳定性优于主成分分析和线性判别分析方法。
This paper presents a new locomotion mode recognition method based on a transformed correlation feature analysis using an electromyography (EMG) pattern. Each movement is recognized using six weighted subcorrelation filters, which are applied to the correlation feature analysis through the use of six time-domain features. The proposed method has a high recognition rate because it reflects the importance of the different features according to the movements and thereby enables one to recognize real-time EMG patterns, owing to the rapid execution of the correlation feature analysis. The experiment results show that the discriminating power of the proposed method is 85.89% (+/- 2.5) when walking on a level surface, 96.47% (+/- 0.9) when going up stairs, and 96.37% (+/- 1.3) when going down stairs for given normal movement data. This makes its accuracy and stability better than that found for the principal component analysis and linear discriminant analysis methods.