Model-Based Step Length Estimation Using a Pendant-Integrated Mobility Sensor.

Markus Lueken,Steffen Leonhardt,Nikolai Weber,Johannes Loeser,Cornelius Bollheimer,Chuong Ngo

doi:10.1109/tnsre.2021.3133535

Abstract

The step length is an important parameter in gait analysis. Long-term monitoring applications for gait analysis are often based on inertial measurement units (IMUs) due to their low-cost and unobtrusive nature. Spatial gait parameters, such as step or stride length, are therefore not directly accessible. In this contribution, we focus on model-based algorithms for step length estimation based on a pendant-integrated IMU during slow walking speeds. We present a model-based approach to estimate the step length, which is divided into two successive steps. As the first part of our approach, we present an algorithm for estimation of the vertical displacement of the center of mass (CoM) during gait. Based on this estimate, we present a novel approach to estimate the step length, which we have deduced from a previously published, simplified gait model. The algorithm is compared to a commonly known approach for accelometry-based step length prediction and validated against reference data obtained from a force plate-integrated treadmill for gait analysis during a clinical study with ten healthy subjects. Due to the applicability to gait stability assessment in elderly or gait impaired patients, we focus on slow walking speeds (1-4 km h-1). The presented algorithms outperform the existing approach and the proposed model calculations provide a more accurate prediction. For the vertical displacement, we achieved a precision of 9.3% (CoV) with an RMSE of 1.5 mm in terms of the trajectory amplitude during normal gait patterns. The step length estimation yields satisfying results with a relative prediction error of lower than 10% for walking speeds of 2-4kmh-1.

Highlights

T HE stride length or step length are important parameters for the assessment of gait quality and gait stability and has proven significance as a long-term predictor for fall risk [1]
We present a twopart approach for estimation of the step length: the first part consists of an algorithm for the estimation of the vertical center of mass (CoM) displacement and the second part implements an approach for step length estimation based on an inverted pendulum model
We identify the direction cosine matrix (DCM) hCs ∈ R3×3, which rotates the inertial sensor data from the local sensor coordinate system s into the target human coordinate system h, where the vertical axis is aligned with the z-axis of the sensor

Summary

Introduction

T HE stride length or step length are important parameters for the assessment of gait quality and gait stability and has proven significance as a long-term predictor for fall risk [1]. It is important to note that the stride length is defined as the distance between two consecutive foot contacts of the same foot, where the step length describes the distance between the ground contact points of the two feet during double stance. In standardized gait analysis procedures, such as stationary motion laboratories, the stride length is often obtained using optical motion-tracking systems or force sensitive elements. These systems yield highly accurate measurements and, are gold standard for the acquisition of gait parameters.

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