Real-Time Joint Axes Estimation of the Hip and Knee Joint during Gait using Inertial Sensors

Abstract

Inertial Measurement Units (IMUs) have proven to be a promising candidate for joint kinematics assessment during human locomotion. The benefits associated with IMU-based joint angle measurements are ease of handling, flexibility and low cost. However, a known limitation is that the joint axes in terms of the attached IMUs need to be identified in order to decompose IMU measurements into joint angles. Conventionally, careful alignment of the IMUs with respect to the body segments and/or calibration motions are required. In this paper, a novel approach is proposed to estimate the joint axes of the hip and knee joint during gait. Our method is easy to use, self-calibrating and real-time capable using the obtained IMU data during gait. In addition to prior methods, the algorithm profits from the periodicity during gait in order to deal with three (rotational) degrees of freedom (3-DoF) motions. Experiments with 8 healthy subjects walking on a motor-driven treadmill have been conducted. The joint axes converged onto the expected axes in all trials and the convergence times averaged less than 15 seconds.