Linear and Non-linear Dimensionality-Reduction Techniques on Full Hand Kinematics
Linear and Non-linear Dimensionality-Reduction Techniques on Full Hand Kinematics
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DOI：
10.3389/fbioe.2020.00429
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发表时间：
2020-05-05
影响因子：
5.7
通讯作者：
Rombokas, Eric
中科院分区：
文献类型：
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作者：
Portnova-Fahreeva, Alexandra A.;Rizzoglio, Fabio;Rombokas, Eric
本研究的目的是找到手部运动学数据的简约表示，以促进假手控制。主成分分析（PCA）和非线性自动编码器网络（nAEN）在捕捉广泛的手势和动作的基本特征的有效性进行了比较。这两种方法的性能进行了比较（a）的能力，准确地重建的手运动数据从一个潜在的流形的减少的维度，（B）跨潜在的维度的方差分布，和（c）的可分离性的手运动的压缩和重建表示使用线性分类器。的nAEN表现出更高的性能比PCA在其能力，更准确地重建手运动学数据从一个潜在的流形减少的维度。然而，对于潜在流形中的二维，PCA能够解释输入数据方差的78%，nAEN占94%。此外，与PCA相比，nAEN潜在流形由具有更均匀的信号方差份额的坐标跨越。最后，nAEN能够产生比PCA更可分离的运动的流形，因为不同的任务在重建时，通过线性分类器SoftMax回归更容易区分。它的结论是，非线性降维可能提供一个更有效的平台比线性方法来控制假手。
The purpose of this study was to find a parsimonious representation of hand kinematics data that could facilitate prosthetic hand control. Principal Component Analysis (PCA) and a non-linear Autoencoder Network (nAEN) were compared in their effectiveness at capturing the essential characteristics of a wide spectrum of hand gestures and actions. Performance of the two methods was compared on (a) the ability to accurately reconstruct hand kinematic data from a latent manifold of reduced dimension, (b) variance distribution across latent dimensions, and (c) the separability of hand movements in compressed and reconstructed representations derived using a linear classifier. The nAEN exhibited higher performance than PCA in its ability to more accurately reconstruct hand kinematic data from a latent manifold of reduced dimension. Whereas, for two dimensions in the latent manifold, PCA was able to account for 78% of input data variance, nAEN accounted for 94%. In addition, the nAEN latent manifold was spanned by coordinates with more uniform share of signal variance compared to PCA. Lastly, the nAEN was able to produce a manifold of more separable movements than PCA, as different tasks, when reconstructed, were more distinguishable by a linear classifier, SoftMax regression. It is concluded that non-linear dimensionality reduction may offer a more effective platform than linear methods to control prosthetic hands.