Dimensionality Reduction of Human Gait for Prosthetic Control.
Dimensionality Reduction of Human Gait for Prosthetic Control.
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DOI：
10.3389/fbioe.2021.724626
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发表时间：
2021
影响因子：
5.7
通讯作者：
Rombokas E
中科院分区：
文献类型：
--
作者：
Boe D;Portnova-Fahreeva AA;Sharma A;Rai V;Sie A;Preechayasomboon P;Rombokas E
我们试图使用降维来简化控制下肢假肢的困难任务。虽然已经描述了许多降维技术，但尚不清楚哪种技术最适合于人类步态数据。在这项研究中，我们首先比较了主成分分析（PCA）和自动编码器的姿态（姿态AE）转换人体运动学数据在平地和楼梯行走。其次，我们比较了PCA，Pose-AE和一种新的自动编码器的性能训练的全人类运动轨迹（Move-AE），以捕捉步态的时变特性。我们比较这些方法的运动分类和识别的个人。这些是识别用于假肢控制的有用数据表示的关键能力。我们首先发现，Pose-AE在降维方面优于PCA，因为它在平地行走数据、楼梯数据和无方向自然运动中实现了更高的方差占比（VAF）。然后，我们发现在我们的第二个任务，移动AE显着优于PCA和姿势AE的运动分类和个人识别任务。这表明自动编码器比PCA更适合于人类步态的降维，并且可以用于编码整个运动的有用表示以促进假肢控制任务。
We seek to use dimensionality reduction to simplify the difficult task of controlling a lower limb prosthesis. Though many techniques for dimensionality reduction have been described, it is not clear which is the most appropriate for human gait data. In this study, we first compare how Principal Component Analysis (PCA) and an autoencoder on poses (Pose-AE) transform human kinematics data during flat ground and stair walking. Second, we compare the performance of PCA, Pose-AE and a new autoencoder trained on full human movement trajectories (Move-AE) in order to capture the time varying properties of gait. We compare these methods for both movement classification and identifying the individual. These are key capabilities for identifying useful data representations for prosthetic control. We first find that Pose-AE outperforms PCA on dimensionality reduction by achieving a higher Variance Accounted For (VAF) across flat ground walking data, stairs data, and undirected natural movements. We then find in our second task that Move-AE significantly outperforms both PCA and Pose-AE on movement classification and individual identification tasks. This suggests the autoencoder is more suitable than PCA for dimensionality reduction of human gait, and can be used to encode useful representations of entire movements to facilitate prosthetic control tasks.