Cortical activity and gait parameter characteristics in people with multiple sclerosis during unobstructed gait and obstacle avoidance

Santinelli, Felipe Balistieri; Sebastião, Émerson; Kuroda, Marina Hiromi; Moreno, Vinicius Christianini; Pilon, Julia; Vieira, Luiz Henrique Palucci; Barbieri, Fábio Augusto

doi:10.1016/j.gaitpost.2021.03.026

Cited by 10 publications

(10 citation statements)

References 25 publications

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“…For continuous monitoring of the gait function and identification of early gait abnormalities, a variety of diagnostic tests and specific digital systems for gait analysis are necessary [22]. Digital deep phenotyping of gait can be achieved by motion capture systems based on optoelectronic stereophotogrammetry, accelerometers or force sensors which were found to be more sensitive than the T25FW for differentiating between patients and healthy controls [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

et al. 2021

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In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (k = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (k = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

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Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

et al. 2021

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“…Moreover, the pre-crossing phase of obstacle negotiation requires visually-dependent motor planning for step regulation that is attentionally demanding [ 42 ]. As cognitive processing impairments may have a negative impact on postural activity and anticipatory gait control before crossing an obstacle [ 15 , 43 ], the deficits in attention and executive function found in PwMS [ 44 ] may be a potential cause of trip-related falls in this population and warrants further exploration.…”

Section: Discussionmentioning

confidence: 99%

“…This is especially important as, relative to non-fallers, fallers with MS poorly control their center of mass over their base of support and their foot swing is closer to the ground when stepping [ 14 ]. While Santinelli et al [ 15 ] have examined gait during obstacle avoidance in PwMS, the gait changes they found between unobstructed walking and obstacle avoidance only included the leading and trailing step during crossing. Understanding how PwMS approach and continue walking after higher risk daily tasks is relevant and ecologically important.…”

Section: Introductionmentioning

confidence: 99%

“…In previous studies, wearable devices have been used to quantify straight line walking gait variability and longer duration bouts such as during the 6-min walk test [ 24 ]. However, there have been few studies on gait variability in the negotiation of obstacles and turning, most of which have focused on the negotiation steps [ 15 ] rather than the gait characteristics preceding and succeeding obstacles and turns.…”

Section: Introductionmentioning

confidence: 99%

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A Preliminary Investigation of the Effects of Obstacle Negotiation and Turning on Gait Variability in Adults with Multiple Sclerosis

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et al. 2021

Sensors

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Many falls in persons with multiple sclerosis (PwMS) occur during daily activities such as negotiating obstacles or changing direction. While increased gait variability is a robust biomarker of fall risk in PwMS, gait variability in more ecologically related tasks is unclear. Here, the effects of turning and negotiating an obstacle on gait variability in PwMS were investigated. PwMS and matched healthy controls were instrumented with inertial measurement units on the feet, lumbar, and torso. Subjects completed a walk and turn (WT) with and without an obstacle crossing (OW). Each task was partitioned into pre-turn, post-turn, pre-obstacle, and post-obstacle phases for analysis. Spatial and temporal gait measures and measures of trunk rotation were captured for each phase of each task. In the WT condition, PwMS demonstrated significantly more variability in lumbar and trunk yaw range of motion and rate, lateral foot deviation, cadence, and step time after turning than before. In the OW condition, PwMS demonstrated significantly more variability in both spatial and temporal gait parameters in obstacle approach after turning compared to before turning. No significant differences in gait variability were observed after negotiating an obstacle, regardless of turning or not. Results suggest that the context of gait variability measurement is important. The increased number of variables impacted from turning and the influence of turning on obstacle negotiation suggest that varying tasks must be considered together rather than in isolation to obtain an informed understanding of gait variability that more closely resembles everyday walking.

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“…In Multiple Sclerosis (MS), the highly variable distribution of demyelination areas and axonal loss in the central nervous system (CNS) can lead to very complex and unpredictable neurological deficits and clinical patterns [1]. Gait disorders as reduced speed and stride length, gait asymmetry, increased muscular energy expenditure, balance deficit, and increased risk of falling represent one of the most disabling aspects [2][3][4]. These motor problems strongly influence the level of independence that a person affected by MS is able to achieve, resulting in a severe negative impact on the quality of life [5].…”

Section: Introductionmentioning

confidence: 99%

Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial

et al. 2021

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Background and Objectives: Gait disorders represent one of the most disabling aspects in multiple sclerosis (MS) that strongly influence patient quality of life. The improvement of walking ability is a primary goal for rehabilitation treatment. The aim of this study is to evaluate the effectiveness of robot-assisted gait training (RAGT) in association with physiotherapy treatment in patients affected by MS in comparison with ground conventional gait training. Study design: Randomized controlled crossover trial. Materials and Methods: Twenty-seven participants affected by MS with EDSS scores between 3.5 and 7 were enrolled, of whom seventeen completed the study. They received five training sessions per week over five weeks of conventional gait training with (experimental group) or without (control group) the inclusion of RAGT. The patients were prospectively evaluated before and after the first treatment session and, after the crossover phase, before and after the second treatment session. The evaluation was based on the 25-foot walk test (25FW, main outcome), 6 min walk test (6MWT), Tinetti Test, Modified Ashworth Scale, and modified Motricity Index for lower limbs. We also measured disability parameters using Functional Independence Measure and Quality of Life Index, and instrumental kinematic and gait parameters: knee extensor strength, double-time support, step length ratio; 17 patients reached the final evaluation. Results: Both groups significantly improved on gait parameters, motor abilities, and autonomy recovery in daily living activities with generally better results of RAGT over control treatment. In particular, the RAGT group improved more than control group in the 25FW (p = 0.004) and the 6MWT (p = 0.022). Conclusions: RAGT is a valid treatment option that in association with physiotherapy could induce positive effects in MS-correlated gait disorders. Our results showed greater effectiveness in recovering gait speed and resistance than conventional gait training.

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Cortical activity and gait parameter characteristics in people with multiple sclerosis during unobstructed gait and obstacle avoidance

Cited by 10 publications

References 25 publications

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

A Preliminary Investigation of the Effects of Obstacle Negotiation and Turning on Gait Variability in Adults with Multiple Sclerosis

Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial

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