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 nonlinear 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.

Gait Analysis Using Wearable Sensors

“…proposed a new method to visualize 3D gait using a stick-chain model and segment orientations estimated from measurements of accelerometers and gyroscopes in a global coordinate system [ 118 ]. In addition, the extended Kalman filter [ 119 ] and Gaussian particle filter [ 120 ] were also used to evaluate the hip angle in a walking cycle from the measurements of the wearable sensors, thus improving accuracy. According to the results of the kinematic analysis, the information on segment acceleration and velocity, joint angle, and gait events, such as heel strike and toe-off, can be provided and used in clinical applications.…”

The Use of Accelerometers and Gyroscopes to Estimate Hip and Knee Angles on Gait Analysis

“…In many applications, the offset drift is solved by means of the Kalman filter. Cikajlo et al proposed an algorithm where the Kalman filter is used to correct the shank inclination measured by the gyroscope [ 14 ]; in addition, the extended Kalman filter [ 15 ] and Gaussian particle filter [ 16 ] were also used to evaluate the hip angle in a walking cycle from the measurements of the wearable sensors, thus improving accuracy. Furthermore, a neural network [ 17 ] was applied for the estimation of ankle, knee and hip joint angles, obtaining good accuracy; however, this method needs training for individual settings before measurements in order to estimate with good accuracy.…”

mDurance: A Novel Mobile Health System to Support Trunk Endurance Assessment

“…IMUs provide raw acceleration, angular rate and magnetic field data that need to be fused together to obtain a sole, optimal estimate of orientation. Diverse algorithms have been proposed in the literature to that end, including Kalman filters [ 63 ], least squares filters [ 64 ] or Gaussian particle filters [ 65 ], among many others [ 66 , 67 ]. The mDurance system particularly implements a recent technique, Madgwick's algorithm [ 68 ], which outperforms most existing approaches in terms of implementation complexity, sampling rate requirements and computational needs.…”