  Prior work demonstrated the potential of using the Linear Predictive Coding
(LPC) filter to approximate muscle stiffness and damping from computer mouse
movements to predict acute stress levels of users. Theoretically, muscle
stiffness and damping in the arm can be estimated using a mass-spring-damper
(MSD) biomechanical model. However, the damping frequency (i.e., stiffness) and
damping ratio values derived using LPC were not yet compared with those from a
theoretical MSD model. This work demonstrates that the damping frequency and
damping ratio from LPC are significantly correlated with those from an MSD
model, thus confirming the validity of using LPC to infer muscle stiffness and
damping. We also compare the stress level binary classification performance
using the values from LPC and MSD with each other and with neural network-based
baselines. We found comparable performance across all conditions demonstrating
LPC and MSD model-based stress prediction efficacy, especially for longer mouse
trajectories. Clinical relevance: This work demonstrates the validity of the
LPC filter to approximate muscle stiffness and damping and predict acute stress
from computer mouse movements.
