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Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment. [ABSTRACT FROM AUTHOR]

Despite the pervasive nature of gait impairment in multiple sclerosis (MS), there is limited information concerning the control of gait termination in individuals with MS. The purpose of this investigation was to examine unplanned gait termination with and without cognitive distractors in individuals with MS compared to healthy controls. Thirty-one individuals with MS and 14 healthy controls completed a series of unplanned gait termination tasks over a pressure sensitive walkway under distracting and non-distracting conditions. Individuals with MS were further broken down into groups based on assistive device use: (no assistive device (MSnoAD) n=18; and assistive device (MSAD) n=13). Individuals with MS who walked with an assistive device (MSAD: 67.8±15.1cm/s) walked slower than individuals without an assistive device (MSnoAD: 110.4±32.3cm/s, p<0.01) and controls (120.0±30.0cm/s; p<0.01). There was a significant reduction in velocity in the cognitively distracting condition (93.4±32.1cm/s) compared to the normal condition [108.8±36.2cm/s; F(1,43)=3.4, p=0.04]. All participants took longer to stop during the distracting condition (1.7±0.6s) than the non-distracting condition (1.4±0.4s; U=673.0 p<0.01). After controlling for gait velocity, post-hoc analysis revealed the MSAD group took significantly longer to stop compared to the control group (p=0.05). Further research investigating the control of unplanned gait termination in MS is warranted. [ABSTRACT FROM AUTHOR]

Background: Mobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices. Methods: A total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA. Results: Mean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6–2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01). Conclusion: BioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking impairment levels and detected differences in gait characteristics by disability level in PwMS. This technology has the potential to provide granular monitoring of gait both inside and outside the clinic. [ABSTRACT FROM AUTHOR]

There has been growing evidence showing gait variability provides unique information about gait characteristics in neurological disorders. This study systemically reviewed and quantitatively synthesized (via meta-analysis) existing evidence on gait variability in various neurological diseases, including Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), cerebellar ataxia (CA), Huntington's disease (HD), multiple sclerosis (MS), and Parkinson's disease (PD). Keyword search were conducted in PubMed, Web of science, Cumulative Index to Nursing and Allied Health Literature, and Cochrane Library. Meta-analysis was performed to estimate the pooled effect size for gait variability for each neurological group. Meta-regression was performed to compare gait variability across multiple groups with neurological diseases. Gait variability of 777 patients with AD, ALS, CA, HD, MS, or PD participating in 25 studies was included in meta-analysis. All pathological groups had increased amount of gait variability and loss of fractal structure of gait dynamics compared to healthy controls, and gait variability differentiated distinctive neurological conditions. The HD groups had the highest alterations in gait variability among all pathological groups, whereas the PD, AD and MS groups had the lowest. Interventions that aim to improve gait function in patients with neurological disorders should consider the heterogeneous relationship between gait variability and neurological conditions. [ABSTRACT FROM AUTHOR]

Gait variability is associated with falls in clinical populations. However, gait variability’s link to falls in persons with Multiple Sclerosis (PwMS) is not well established. This investigation examined the relationship between stride-time variability, fall risk, and physiological fall risk factors in PwMS. 17 PwMS (62.8±7.4 years) and 17 age-matched controls (62.8±5.9 years) performed the 6-minute walk test. Stride-time was assessed with accelerometers attached to the participants’ shanks. Stride-time variability was measured by interstride coefficient of variation (CV) of stride-time. The participant’s fall risk was measured by the short form physiological profile assessment (PPA). A Spearman correlation analysis was used to determine the relationship between variables. Increased fall risk was strongly associated with increased stride-time CV in both PwMS (ρ=0.71, p<0.01) and the controls (ρ=0.67, p<0.01). Fall risk was not correlated with average stride-time (p>0.05). In PwMS, stride-time CV was related to postural sway (ρ=0.74, p<0.01) while in the control group, it was related to proprioception (ρ=0.61, p<0.01) and postural sway (ρ=0.78, p<0.01). Current observations suggest that gait variability is maybe more sensitive marker of fall risk than average gait parameters in PwMS. It was also noted that postural sway may be potentially targeted to modify gait variability in PwMS. [ABSTRACT FROM AUTHOR]