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. Author manuscript; available in PMC: 2017 Jun 1.
Published in final edited form as: IEEE Trans Neural Syst Rehabil Eng. 2016 Apr 14;24(6):621–629. doi: 10.1109/TNSRE.2016.2550860

Table II.

Statistical models for predicting knee joint moment features during crouch gait.

Knee Moment Feature Predictive Equation Adj. R2 CV RMSE p
Weight Acceptance Peak
(KEMWA, Nm)		 KEMWA= −25.0 + 0.55·W +
0.41·θWA + 0.99 ·ΔθWA 		0.99 1.78 <0.001
Mid-Stance Minimum
(KEMMS, Nm)		 KEMMS = 7.64 + 0.026·W·θMS 0.93 4.61 <0.001
Late Stance Peak
(KEMLS, Nm)		 KEMLS = −25.4 + 0.50·W +
0.86·θLS 		0.90 3.80 <0.001
Flexion Stiffness
(kF, Nm/deg)		 KF = −2.33 + 0.082·W + 0.11·θWA
− 0.20·ΔθWA 		0.94 0.53 <0.001
Extension Stiffness
(kE, Nm/deg)		 KE = 0.21 + 0.055·W + 1.37·θWA −
1.38·θMS− 1.38· ΔθMS 		0.98 0.23 <0.001

Where θWA is defined as the peak knee flexion angle during weight acceptance, ΔθWA is defined as the knee angle range of motion from heal strike to the weight acceptance peak, θMS is defined as the minimum knee flexion angle during stance, and θLS is defined as the knee angle at contralateral heal strike. Adj. R2 refers to R2 value for each predictive equation adjusted for the number of predictors in the model. CV RMSE refers to the grand average RMSE across the 10-fold leave one out cross-validation.