Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait

Abstract

BackgroundThe step-by-step determination of the spatio-temporal parameters of gait is clinically relevant since it provides an estimation of the variability of specific gait patterns associated with frequent geriatric syndromes. In recent years, several methods, based on the use of magneto-inertial units (MIMUs), have been developed for the step-by-step estimation of the gait temporal parameters. However, most of them were applied to the gait of healthy subjects and/or of a single pathologic population. Moreover, spatial parameters in pathologic populations have been rarely estimated step-by-step using MIMUs. The validity of clinically suitable MIMU-based methods for the estimation of spatio-temporal parameters is therefore still an open issue. The aim of this study was to propose and validate a method for the determination of both temporal and spatial parameters that could be applied to normal and heavily compromised gait patterns.MethodsTwo MIMUs were attached above each subject’s ankles. An instrumented gait mat was used as gold standard. Gait data were acquired from ten hemiparetic subjects, ten choreic subjects, ten subjects with Parkinson’s disease and ten healthy older adults walking at two different gait speeds. The method detects gait events (GEs) taking advantage of the cyclic nature of gait and exploiting some lower limb invariant kinematic characteristics. A combination of a MIMU axes realignment along the direction of progression and of an optimally filtered direct and reverse integration is used to determine the stride length.ResultsOver the 4,514 gait cycles analyzed, neither missed nor extra GEs were generated. The errors in identifying both initial and final contact at comfortable speed ranged between 0 and 11 ms for the different groups analyzed. The stride length was estimated for all subjects with less than 3% error.ConclusionsThe proposed method is apparently extremely robust since gait speed did not substantially affect its performance and both missed and extra GEs were avoided. The spatio-temporal parameters estimates showed smaller errors than those reported in previous studies and a similar level of precision and accuracy for both healthy and pathologic gait patterns. The combination of robustness, precision and accuracy suggests that the proposed method is suitable for routine clinical use.Electronic supplementary materialThe online version of this article (doi:10.1186/1743-0003-11-152) contains supplementary material, which is available to authorized users.

Mobile Stride Length Estimation With Deep Convolutional Neural Networks

“…The contributions on the double-integration approach covered here ( [9], [10], [13]- [15], [17], [18]) all employ accelerometer and gyroscope data captured at the subjects' feet, Trojaniello et al [14] additionally use magnetometer information. Sampling rates are comparable and between 100 and 200 Hz for all studies.…”

“…forward in time. Only Trojaniello et al [14] use a direct and reverse integration scheme which spawns two integrations -one from the start and one from the end of the current stride -and computes a weighted mean between the two with weights depending on the distance to the next ZUPT. This is said to further resolve integration drifts [20].…”

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

“…In future works, the segmentation could be improved by also using the accelerometer values for detecting the shank verticality [19] or by combining our segmentation algorithm to AHRS(Attitude and Heading Reference System) algorithms for estimating the exact sensor orientation [20] [21]. We could also add a second IMU on the opposite leg and use the swing phase of one leg for robustly detecting the stance phase of the other [10]. The raw signals filtering also turned to be critical.…”

An Accurate Wearable Foot Clearance Estimation System: Toward a Real-Time Measurement System

“…While both 3-IR and IR-IMU configurations showed the superior performance when compared to the other tested configurations, the IR-IMU also benefitted from the ability to estimate other spatiotemporal parameters of gait such as cadence, speed, and step length [21]. This configuration can be used as a multipurpose system for a robust and thorough gait analysis.…”