Feature-Level Fusion of Surface Electromyography for Activity Monitoring.
Feature-Level Fusion of Surface Electromyography for Activity Monitoring.
复制标题
用于活动监测的表面肌电图特征级融合
DOI：
10.3390/s18020614
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发表时间：
2018-02-17
期刊：
影响因子：
--
通讯作者：
Luo Z
中科院分区：
文献类型：
--
作者：
Xi X;Tang M;Luo Z
表面肌电信号由于能有效地反映使用者的运动意图而被广泛应用于活动监测和康复应用中。然而，实时的表面肌电信号是非平稳的，并且在信号的时间范围内变化很大。虽然以往的研究都集中在这些问题上，但结果并不令人满意。因此，我们提出了一种新的方法进行特征级融合，以获得新的表面肌电信号的特征空间。进行包括跌倒在内的8项日常生活活动(ADL)测试，以获得来自肢体的肌电信号的原始数据。将时频域、时频域和熵域相结合的特征集应用于原始数据，建立初始特征空间。提出了一种新的投影方法--加权遗传算法(WGA-GCCA)来获得最终的特征空间。为了评价新特征空间的性能，进行了不同的测试。利用WGA-GCCA构造新的特征空间，在改善单调性的同时，有效地进行降维，动态选择最优特征向量。与几种融合方法相比，基于空间模糊c-均值算法的Davies-Bouldin指数(DBI)得到了最低值。将其应用于支持向量机分类器，也取得了最高的分类精度。
Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation applications as they reflect effectively the motor intentions of users. However, real-time sEMG signals are non-stationary and vary to a large extent within the time frame of signals. Although previous studies have focused on the issues, their results have not been satisfactory. Therefore, we present a new method of conducting feature-level fusion to obtain a new feature space for sEMG signals. Eight activities of daily life (ADLs), including falls, were performed to obtain raw data from EMG signals from the lower limb. A feature set combining the time domain, time–frequency domain, and entropy domain was applied to the raw data to establish an initial feature space. A new projection method, the weighting genetic algorithm for GCCA (WGA-GCCA), was introduced to obtain the final feature space. Different tests were carried out to evaluate the performance of the new feature space. The new feature space created with the WGA-GCCA effectively reduced the dimensions and selected the best feature vectors dynamically while improving monotonicity. The Davies–Bouldin index (DBI) based on fuzzy c-means algorithms of the space obtained the lowest value compared with several fusion methods. It also achieved the highest accuracy when applied to support vector machine classifier.
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影响因子：
5.6
作者：
Frauscher, Birgit;Iranzo, Alex;Hoegl, Birgit
通讯作者：
Hoegl, Birgit
DOI：
10.3390/s17010187
发表时间：
2017-01-19
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Liu KC;Chan CT
通讯作者：
Chan CT
影响因子：
2.3
作者：
Mantilla, Carlos B.;Seven, Yasin B.;Hurtado-Palomino, Juan N.;Zhan, Wen-Zhi;Sieck, Gary C.
通讯作者：
Sieck, Gary C.
影响因子：
8
作者：
Pakhira, MK;Bandyopadhyay, S;Maulik, U
通讯作者：
Maulik, U
影响因子：
7.3
作者：
Ranaee, Vahid;Ebrahimzadeh, Ata;Ghaderi, Reza
通讯作者：
Ghaderi, Reza