Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

Abstract

As low cost and highly portable sensors, inertial measurements units (IMU) have become increasingly used in gait analysis, embodying an efficient alternative to motion capture systems. Meanwhile, being able to compute reliably accurate spatial gait parameters using few sensors remains a relatively complex problematic. Providing a clinical oriented solution, our study presents a gyrometer and accelerometer based algorithm for stride length estimation. Compared to most of the numerous existing works where only an averaged stride length is computed from several IMU, or where the use of the magnetometer is incompatible with everyday use, our challenge here has been to extract each individual stride length in an easy-to-use algorithm requiring only one inertial sensor attached to the subject shank. Our results were validated on healthy subjects and patients suffering from Parkinson's disease (PD). Estimated stride lengths were compared to GAITRite © walkway system data: the mean error over all the strides was less than 6% for healthy group and 10.3% for PD group. This method provides a reliable portable solution for monitoring the instantaneous stride length and opens the way to promising applications.

A sensor fusion approach for inertial sensors based 3D kinematics and pathological gait assessments: toward an adaptive control of stimulation in post-stroke subjects

“…To estimate stride length and velocity for each step in each trial, feet trajectories have to be computed in a global frame. Usually, the solution consists in double integrating 2D linear acceleration in sagittal plane with an angular rate based gait cycle segmentation [13], [22] and an angular rate integration to estimate orientation needed to gravity removal. In our study, pathological gait was often associated to compensatory strategies (e.g.…”

“…optical motion capture system or instrumented mats). In addition, they usually segment gait phases using angular velocity patterns, which is satisfying for normal gait [13] but tends to fail when gait is impaired and subjects develop compensatory strategies [14]. In this study, we propose a novel approach dedicated to pathological motion assessment from IMUs in 26 participants with post-stroke hemiplegia, integrating two aspects: 1) robust stance phase detection based on acceleration and angular rate combination and 2) estimation of joint angles based on an AHRS algorithm and gravity cancellation for reconstructing 3D trajectory of individual steps.…”

The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units

“…Unsupervised data capture requires extended battery life (Najafi, 2011), methods for limiting sensor placement error (Vanegas and Stirling, 2015), and analytic methods for extracting gait parameters from IMU data. A review of the literature shows a fivesensor system attached to shanks, thighs, and sacrum can provide sufficient information for estimation of center of mass (Westerdijk et al, 2012), spatial gait parameters (Aminian et al, 2002;Li et al, 2009;Sijobert et al, 2015), and joint angle dynamics (Seel et al, 2014), as well as temporal gait parameters (Aminian et al, 2002).…”

FES-Drop-Foot Correction: From Pre-programmed Patterns to Online Modulation

“…This work has now to be extended and validated experimentally in real-time conditions. In other studies we have also shown that other gait parameters can be obtained from one inertial sensor such a stride length [7], foot clearance [8]. This type of information as well as foot inclination can be integrated in optimal controllers to online compute the FES pattern for the upcoming stride.…”