Gaussian particle filter for tracking hip angle in gait cycles

Hip angle has attracted increasing attention recently because of its wide spectrum of applications, including gait analysis, clinical performance analysis and animation. Accurate and robust estimation of hip angle in ambulatory environment remains a challenge because the non-linear nature of thigh movement has not been well studied yet. We propose to use Hybrid Dynamic Bayesian Network (HDBN) to model the nonlinear hip angle dynamics. Based on the model, Gaussian Particle Filter (GPF) is designed 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 with four subjects and the results have shown that the proposed methods can achieve robust hip angle tracking for different subjects with significant accuracy improvement over the previous work on the ambulatory gait analysis.