Measuring Gait Stability in People with Multiple Sclerosis Using Different Sensor Locations and Time Scales

Effects of the kinematic variable, time delay and data length on test-retest reliability of the maximal Lyapunov exponent of human walking

The maximal Lyapunov exponent (MLE) has been used to quantify the dynamic stability of human locomotion. The method for estimating MLE requires selecting a proper time series of kinematic variables and reconstructing phase space using proper time delay. The data length also affects the reliability of the measured MLE. However, there has been no criterion for the choice of the time series, time delay or data length. Here, we quantified the effect of these factors on the test-retest reliability of MLE estimations. We recruited 15 young and healthy adults and let them walk on a treadmill three times. We calculated MLE employing various lengths of time series of 18 frequently used kinematic variables and two typical choices of time delay: fixed delay and delay selected by average mutual information algorithm. Then, we measured the intraclass correlation coefficient (ICC) of the measured MLE under each condition. Our results show that the choice of time delay does not affect reliability. Five among the 18 kinematic variables enabled excellent reliability with ICC above 0.9 within 450 strides and also enabled ICC above 0.75 even with 60 or less strides. These findings can contribute to establishing the criteria for measuring the dynamic stability of human walking.

Stability of sensor-based gait parameters reassessed after a period of one year in people with multiple sclerosis

BACKGROUND

Currently, there are several studies showing that wearable inertial sensors are highly sensitive in the detection of gait disturbances in people with multiple sclerosis (PwMS), showing excellent reliability within one or 7-14 days. However, it is not known how stable these gait parameters remain over a longer period of time. This is surprising, because many treatments last longer than two weeks. Thus, the purpose of the current study was to examine gait parameters obtained by means of wearable inertial sensors during a 6-min walk and to reassess these parameters after a period of one year.

METHODS

Fifty PwMS (without a relapse or a recent change in the Expanded Disability Status Scale (EDSS) or treatment) and 20 healthy participants were examined at two assessment points (interval between assessments: 14.4 ± 6.6 months). At each assessment point, all participants had to complete a 6-min walking test, an observer-rater test (Berg Balance Scale, BBS) and a Timed-up and Go Test (TUG). To measure mean gait parameters (i.e. walking speed, stride length, stride time, the duration of the stance and swing phase and minimum toe-to-floor distance), as well as the intraindividual standard deviation of each mean gait parameter, wearable inertial sensors were utilized.

RESULTS

We found that even after one year all mean gait parameters showed excellent Intraclass Correlation Coefficients (ICC between 0.75 and 0.95) in PwMS. Looking at MS subgroups, the ICCs were slightly higher in MS subgroup 2 (EDSS 2.0-5.0) than those in MS subgroup 1 (EDSS 0.0-1.5) and healthy controls. Compared to the mean gait parameters, parameters of gait variability showed only good-to-fair ICC values in PwMS. Concerning BBS and TUG, the ICC values after one year were close to the ICC values of the measured mean gait parameters.

CONCLUSIONS

Due to the excellent stability of mean gait parameters after one year, these sensor-based gait parameters can be identified as clinically relevant markers to evaluate treatment effects over a longer (several months) period of time in MS.

Optimal sensor location and direction to accurately classify people with early-stage multiple sclerosis using gait stability

BACKGROUND

The local divergence exponent (LDE) has been used to assess gait stability in people with multiple sclerosis (pwMS). Although previous studies have consistently found that stability is lower in pwMS, inconsistent methodologies have been used to assess patients with a broad range of disability levels.

QUESTIONS

What sensor location and movement direction(s) are better able to classify pwMS at early stages of the disease?

METHODS

49 pwMS with EDSS ≤ 2.5 and 24 healthy controls walked overground for 5 min while 3D acceleration data was obtained from sensors placed at the sternum (STR) and lumbar (LUM) areas. Unidirectional (vertical [VT], mediolateral [ML], and anteroposterior [AP]) and 3-dimensional (3D) LDEs were calculated using STR and LUM data over 150 strides. ROC analyses were performed to assess classification models using single and combined LDEs, with and without velocity per lap (VEL_LAP) as a covariate.

RESULTS

Four models performed equally well by using combinations of VEL_LAP, LUM_3D, LUM_VT, LUM_ML, LUM_AP, STR_ML, and STR_AP (AUC = 0.879). The best model using single sensor LDEs included VEL_LAP, STR_3D, STR_ML, and STR_AP (AUC = 0.878), whereas using VEL_LAP + STR_VT (AUC = 0.869) or VEL_LAP + STR_3D (AUC=0.858) performed best using a single LDE.

SIGNIFICANCE

The LDE offers an alternative to currently insensitive tests of gait impairment in pwMS at early stages, when deterioration is not clinically evident. For clinical purposes, the implementation of this measure can be simplified using a single sensor at the sternum and a single LDE measure, but speed should be considered. Longitudinal studies to determine the predictive power and responsiveness of the LDE to MS progression are still needed.

Validity of an inertial sensor-based system for the assessment of spatio-temporal parameters in people with multiple sclerosis

Background

Gait variability in people with multiple sclerosis (PwMS) reflects disease progression or may be used to evaluate treatment response. To date, marker-based camera systems are considered as gold standard to analyze gait impairment in PwMS. These systems might provide reliable data but are limited to a restricted laboratory setting and require knowledge, time, and cost to correctly interpret gait parameters. Inertial mobile sensors might be a user-friendly, environment- and examiner-independent alternative. The purpose of this study was to evaluate the validity of an inertial sensor-based gait analysis system in PwMS compared to a marker-based camera system.

Methods

A sample N = 39 PwMS and N = 19 healthy participants were requested to repeatedly walk a defined distance at three different self-selected walking speeds (normal, fast, slow). To measure spatio-temporal gait parameters (i.e., walking speed, stride time, stride length, the duration of the stance and swing phase as well as max toe clearance), an inertial sensor system as well as a marker-based camera system were used simultaneously.

Results

All gait parameters highly correlated between both systems (r > 0.84) with low errors. No bias was detected for stride time. Stance time was marginally overestimated (bias = -0.02 ± 0.03 s) and gait speed (bias = 0.03 ± 0.05 m/s), swing time (bias = 0.02 ± 0.02 s), stride length (0.04 ± 0.06 m), and max toe clearance (bias = 1.88 ± 2.35 cm) were slightly underestimated by the inertial sensors.

Discussion

The inertial sensor-based system captured appropriately all examined gait parameters in comparison to a gold standard marker-based camera system. Stride time presented an excellent agreement. Furthermore, stride length and velocity presented also low errors. Whereas for stance and swing time, marginally worse results were observed.

Gait stability reflects motor tracts damage at early stages of multiple sclerosis

Background:

Gait in people with multiple sclerosis (PwMS) is affected even when no changes can be observed on clinical examination. A sensitive measure of gait deterioration is stability; however, its correlation with motor tract damage has not yet been established.

Objective:

To compare stability between PwMS and healthy controls (HCs) and determine associations between stability and diffusion magnetic resonance image (MRI) measures of axonal damage in selected sensorimotor tracts.

Methods:

Twenty-five PwMS (Expanded Disability Status Scale (EDSS) < 2.5) and 15 HCs walked on a treadmill. Stability from sacrum (LDESAC), shoulder (LDESHO) and cervical (LDECER) was calculated using the local divergence exponent (LDE). Participants underwent a 7T-MRI brain scan to obtain fibre-specific measures of axonal loss within the corticospinal tract (CST), interhemispheric sensorimotor tract (IHST) and cerebellothalamic tract (CTT). Correlation analyses between LDE and fibre density (FD) within tracts, fibre cross-section (FC) and FD modulated by FC (FDC) were conducted. Between-groups LDE differences were analysed using analysis of variance (ANOVA).

Results:

Correlations between all stability measures with CSTFD, between CSTFDC with LDESAC and LDECER, and LDECER with IHSTFD and IHSTFDC were significant yet moderate ( R < −0.4). Stability was significantly different between groups.

Conclusions:

Poorer gait stability is associated with corticospinal tract (CST) axonal loss in PwMS with no-to-low disability and is a sensitive indicator of neurodegeneration.

A topological data analysis-based method for gait signals with an application to the study of multiple sclerosis

In the past few years, light, affordable wearable inertial measurement units have been providing to clinicians and researchers the possibility to quantitatively study motor degeneracy by comparing gait trials from patients and/or healthy subjects. To do so, standard gait features can be used but they fail to detect subtle changes in several pathologies including multiple sclerosis. Multiple sclerosis is a demyelinating disease of the central nervous system whose symptoms include lower limb impairment, which is why gait trials are commonly used by clinicians for their patients’ follow-up. This article describes a method to compare pairs of gait signals, visualize the results and interpret them, based on topological data analysis techniques. Our method is non-parametric and requires no data other than gait signals acquired with inertial measurement units. We introduce tools from topological data analysis (sublevel sets, persistence barcodes) in a practical way to make it as accessible as possible in order to encourage its use by clinicians. We apply our method to study a cohort of patients suffering from progressive multiple sclerosis and healthy subjects. We show that it can help estimate the severity of the disease and also be used for longitudinal follow-up to detect an evolution of the disease or other phenomena such as asymmetry or outliers.

Wearable inertial sensors are highly sensitive in the detection of gait disturbances and fatigue at early stages of multiple sclerosis

Background

The aim of the current study was to examine multiple gait parameters obtained by wearable inertial sensors and their sensitivity to clinical status in early multiple sclerosis (MS). Further, a potential correlation between gait parameters and subjective fatigue was explored.

Methods

Automated gait analyses were carried out on 88 MS patients and 31 healthy participants. To measure gait parameters (i.e. walking speed, stride length, stride duration, duration of stance and swing phase, minimal toe-to-floor distance), wearable inertial sensors were utilized throughout a 6-min 25-ft walk. Additionally, self-reported subjective fatigue was assessed.

Results

Mean gait parameters consistently revealed significant differences between healthy participants and MS patients from as early as an Expanded Disability Status Scale (EDSS) value of 1.5 onwards. Further, MS patients showed a significant linear trend in all parameters, reflecting continuously deteriorating gait performance throughout the test. This linear deterioration trend showed significant correlations with fatigue.

Conclusions

Wearable inertial sensors are highly sensitive in the detection of gait disturbances, even in early MS, where global scales such as the EDSS do not provide any clinical information about deviations in gait behavior. Moreover, these measures provide a linear trend parameter of gait deterioration that may serve as a surrogate marker of fatigue. In sum, these results suggest that classic timed walking tests in routine clinical practice should be replaced by readily and automatically applicable gait assessments, as provided by inertial sensors.