Table 1 Variance explained.

From: A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement

Kinematic feature

Variance explained (%)

Linear acceleration, Y-axis (\(\bf P_{a_{Y}}\))

26.8

Linear velocity, Y-axis (\(\bf P_{v_{Y}}\))

23.1

Angular velocity, Yaw-axis (\(\bf P_{\omega _{Yaw}}\))

15.9

Angular velocity, Roll-axis (\(\bf P_{\omega _{Roll}}\))

15.1

Angular acceleration, Yaw-axis (\(\bf P_{\alpha _{Yaw}}\))

10.1

Angular acceleration, Roll-axis (\(P_{\alpha _{Roll}}\))

2.9

Angular acceleration, X-axis (\(P_{a_{X}}\))

2.5

Angular velocity, Pitch-axis (\(P_{\omega _{Pitch}}\))

1.8

Angular acceleration, Pitch-axis (\(P_{\alpha _{Pitch}}\))

0.9

Linear velocity, X-axis (\(P_{v_{X}}\))

0.9

Linear acceleration, Z-axis (\(P_{a_{Z}}\))

0.7

Linear velocity, Z-axis (\(P_{v_{Z}}\))

0.2

  1. Table shows the mean variance explained by each kinematic feature used in the feature selection (over a 1000 epochs). Selected features that explain approximately 90% variance are highlighted in bold.
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