Exact(1)
The aim of this study was to evaluate changes in the three dimensional lower limb kinematics during a simulated cycling time trial.
Similar(7)
Muscle activity of VMO and VL, three-dimensional lower-extremity kinematics, and ground reaction forces of healthy recreational athletes (12M, 13F) were recorded during five exercises.
Principal component analysis (PCA) projects the high-dimensional data to lower dimensional space by capturing maximum variance [88].
The basic concept of latent semantic analysis is that mapping texts represented in high-dimensional VSM to lower dimensional latent semantic space.
PCA is a linear projection method that allows visualization of high-dimensional data in a lower dimensional space.
They transform high-dimensional input data into lower dimensional components that capture the most important variations in the original data (Alter et al., 2000; Lee and Batzoglou, 2003; Liebermeister, 2002).
A projection based feedback compression was utilized to project the high-dimensional channel space into a lower dimensional subspace [16].
Finally, a visualization approach is provided for projecting high-dimensional fault information onto a lower dimensional and drawable space.
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