An Orthopaedic Robotic-Assisted Rehabilitation Method of the Forearm in Virtual Reality Physiotherapy

Table 6

Regression models for predicting kinesiology information of the achieved range of motion, the velocity of movements, and exerted opposing forces, as a function of difficulty input parameters.

Movement	Regression predictive model	No significant effect for the model (p > 0.05)
FE	RoM^∗performed = b0 + b1 ∗ RoMwork	ttask ∗ RoMwork
FE	Velocity^∗performed = b0 + b1 ∗ ttask + b2 ∗ RoMwork	—
FE	Force^∗performed = b0 + b1 ∗ Forcework + b2 ∗ ttask ∗ RoMwork	ttask
PS	RoM^∗performed = b0 + b1 ∗ RoMwork	—
PS	Velocity^∗performed = b0 + b1 ∗ sequence + b2 ∗ ballsspeed + b3 ∗ sequence ∗ ballsspeed + b4 ∗ RoMwork ∗ ballsfrequency	ballsfrequency, sequence ∗ ballsnumber