Linear Predictive Coding for Acute Stress Prediction from Computer Mouse Movements

TL;DR

This study validates the use of Linear Predictive Coding (LPC) to estimate muscle properties from mouse movements and predict acute stress, showing comparable performance to biomechanical models and neural network baselines.

Contribution

It demonstrates that LPC-derived parameters correlate with biomechanical models, confirming LPC's effectiveness for stress prediction from mouse movements.

Findings

LPC damping frequency and ratio correlate with MSD model values.

LPC and MSD models achieve similar stress classification accuracy.

LPC-based approach is effective for longer mouse trajectories.

Abstract

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.