Gaussian Process Regression for COP Trajectory Estimation in Healthy and Pathological Gait Using Instrumented Insoles

Research in powered prostheses and orthoses has relied on COP measurements to inform a device's controller about the body's progression through the gait cycle, and to provide sensory substitution for prosthesis users, thereby helping them maintain balance during locomotion. Obtaining accurate COP measurements in out-of-the-lab contexts currently requires pressure sensitive insoles with dense arrays of sensing elements, which are expensive and bulky, limiting the accessibility and scalability of this technology. In this paper, we present a new method to reconstruct COP trajectories in over-ground walking tasks, using an affordable sensor array with eight sensing elements embedded in shoe insoles. The method leverages Gaussian Process Regression (GPR) to perform predictions from raw sensor data using Bayesian inference. A preliminary validation was carried out with a convenience sample of healthy individuals and patients with neuromuscular disorders. Combined mediolateral (ML) and anteroposterior (AP) errors where 2% and 3% for healthy individuals and patients, respectively. The analysis evidenced larger stride-to-stride variability in the ML COP excursion for the patient group, suggesting higher levels of motor noise associated with selective muscle weakness. These promising results indicate the potential of the proposed method to accurately estimate COP trajectories for future applications in wearable robotics and out-of-the-lab clinical gait assessments.