Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals.
Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals.
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DOI：
10.3390/s110201721
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Barshan B
中科院分区：
文献类型：
--
作者：
Ayrulu-Erdem B;Barshan B
利用离散小波变换（DWT）分解提取陀螺仪信号的信息特征，并将其作为多层前馈人工神经网络（ann）的输入进行腿部运动分类。由于小波变换是基于分析信号与原型小波函数的关联，因此小波类型的选择对基于小波变换的应用的性能有很大影响。我们还研究了选择不同小波族对分类精度和人工神经网络复杂度的影响，并对它们进行了比较。16阶的Daubechies小波和3.1阶的反向双正交（RBO）小波的分类准确率最高，达到97.7%，两者的ANN复杂度相似。然而，RBO 3.1小波是更可取的，因为它在DWT分解和重建中的计算复杂度较低。
We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction.
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影响因子：
2
作者：
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通讯作者：
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