Moving Past Principal Component Analysis: Nonlinear Dimensionality Reduction Towards Better Hand Pose Synthesis
Moving Past Principal Component Analysis: Nonlinear Dimensionality Reduction Towards Better Hand Pose Synthesis
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DOI：
10.1109/ismr48347.2022.9807580
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Edoardo Battaglia;Michael Kasman;A. M. Fey
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文献类型：
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作者：
Edoardo Battaglia;Michael Kasman;A. M. Fey
尽管它们具有许多自由度的复杂运动学结构，但人类的手已被证明具有协同行为，协调的关节运动能够解释手姿势测量中的大量变化。这种现象传统上通过主成分分析（PCA）进行分析，并导致了医疗机器人中的重要应用，例如上肢假肢的设计和控制以及减少传感器数量的手部姿势测量。然而，使用更复杂的，非线性降维技术的手关节测量已在文献中探索不足。在本文中，我们的目标是通过比较主成分分析，核主成分分析（KPCA），和自动编码器在同一数据集上，重建的手构成相对于原始数据集的均方误差方面的性能进行评估，以填补这一空白。结果表明，非线性技术的性能更好，降低均方误差高达25%的KPCA和50%的自动编码器相比，PCA。与其他两种方法相比，重建的姿态的可视化显示出更好的能力，从自动编码器重建手的形状。
Despite their complex kinematic structure with many degrees of freedom, human hands have been shown to have synergistic behavior, with coordinated joint movements being able to explain a large amount of the variance in hand posture measurements. This phenomenon has traditionally been analyzed through Principal Component Analysis (PCA), and has led to important applications in medical robotics, such as the design and control of upper limb prostheses and measurement of hand posture with a reduced number of sensors. However, the use of more complex, nonlinear dimensionality reduction techniques for hand joint measurements has been under-explored in the literature. In this paper, we aim to fill this gap by comparing Principal Component Analysis, Kernel Principal Component Analysis (KPCA), and autoencoders on the same data set, evaluating the performance in terms of Mean Square Error of reconstructed hand poses with respect to the original data set. Results show a better performance for the nonlinear techniques, lowering Mean Square Error up to 25% for the KPCA and 50% for the autoencoders when compared to PCA. Visualization of the reconstructed poses shows a better ability from the autoencoder to reconstruct hand shapes when compared to the two other methods.