Ambulatory Hip Angle Estimation using Gaussian Particle Filter

Abstract

Hip angle is a major parameter in gait analysis while gait analysis plays an important role in healthcare, animation and other applications. Accurate estimation of hip angle using wearable inertial sensors in ambulatory environment remains a challenge because 1) the non-linear nature of thigh movement has not been well addressed, and 2) the variation of micro-inertial sensor measurement noise has not been studied yet. We propose to use Hybrid Dynamic Bayesian Network (HDBN) to model the non-linear hip angle dynamics and variation of measurement noise levels, and Gaussian Particle Filter (GPF) to estimate the hip angle during gait cycles from the measurements of the wearable accelerometers that are attached to the thighs. The experiments have been conducted and the results have shown that the proposed method can achieve significant accuracy improvement over the previous work on the ambulatory hip angle estimation.