Wearable sensors for realtime accurate hip angle estimation

Hip angle is a major parameter in gait analysis while gait analysis plays important role in health-care, animation and other applications. 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. Although piece-wise linear model is effective to approximate the non-linear model, the current solutions, Gaussian Particle Filter (GPF), is suffering from heavily computation load, which makes the ambulatory hip angle estimation in real time impossible. In this paper, we propose to use Discrete Wavelet Transform to detect major gait events from the measurements of the wearable accelerometer that are attached to the thigh. Based on the detection result, a corresponding linear hip angle dynamic is selected and an Unscented Kalman Filter (UKF) is invoked to estimate the hip angle. The experimental results have shown that the proposed methods can achieve robust and accurate hip angle estimation, and with much less computation loads over the previous work on the ambulatory gait analysis.