Using machine learning algorithms to detect fear of falling in people with multiple sclerosis in standardized gait analysis.

Introduction: Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. The progressive impairment of gait is one of the most important pathognomic symptoms which are associated with falls and fear of falling (FOF) in people with MS (pwMS). 60 % of pwMS show a FOF, which leads to restrictions in mobility as well as physical activity and reduces the quality of life in general.

The Association of Age, Sex, and BMI on Lower Limb Neuromuscular and Muscle Mechanical Function in People with Multiple Sclerosis.

(1) Background: The countermovement jump (CMJ) on a force plate could be a sensitive assessment for detecting early lower-limb muscle mechanical deficits in the early stages of multiple sclerosis (MS). CMJ performance is known to be influenced by various anthropometric, physiological, and biomechanical factors, mostly investigated in children and adult athletes. Our aim was to investigate the association of age, sex, and BMI with muscle mechanical function using CMJ to provide a comprehensive overview of lower-limb motor function in people with multiple sclerosis (pwMS).

Sensitive Identification of Asymmetries and Neuromuscular Deficits in Lower Limb Function in Early Multiple Sclerosis.

Background: In the early stages of multiple sclerosis (MS), there are no objective sensitive functional assessments to identify and quantify early subclinical neuromuscular deficits and lower limb strength asymmetries during complex movements. Single-countermovement jumps (SLCMJ), a maximum single leg vertical jump, on a force plate allow functional evaluation of unilateral lower limb performance in performance diagnostics and could therefore provide early results on asymmetries in MS.

Objective: Objective evaluation of early lower limb neuromuscular deficits and asymmetries in people with multiple sclerosis (pwMS) using SLCMJ on a force plate.

Countermovement Jumps Detect Subtle Motor Deficits in People with Multiple Sclerosis below the Clinical Threshold.

In the early stages of multiple sclerosis (MS), there are currently no sensitive assessments to evaluate complex motor functions. The countermovement jump (CMJ), a high-challenge task in form of a maximal vertical bipedal jump, has already been investigated as a reliable assessment in healthy subjects for lower extremity motor function. The aim was to investigate whether it is possible to use CMJ to identify subthreshold motor deficits in people with multiple sclerosis (pwMS).

Detection of Fall Risk in Multiple Sclerosis by Gait Analysis-An Innovative Approach Using Feature Selection Ensemble and Machine Learning Algorithms.

One of the common causes of falls in people with Multiple Sclerosis (pwMS) is walking impairment. Therefore, assessment of gait is of importance in MS. Gait analysis and fall detection can take place in the clinical context using a wide variety of available methods.

Transparent Quality Optimization for Machine Learning-Based Regression in Neurology.

The clinical monitoring of walking generates enormous amounts of data that contain extremely valuable information. Therefore, machine learning (ML) has rapidly entered the research arena to analyze and make predictions from large heterogeneous datasets. Such data-driven ML-based applications for various domains become increasingly applicable, and thus their software qualities are taken into focus.

Digital Biomarkers in Multiple Sclerosis.

For incurable diseases, such as multiple sclerosis (MS), the prevention of progression and the preservation of quality of life play a crucial role over the entire therapy period. In MS, patients tend to become ill at a younger age and are so variable in terms of their disease course that there is no standard therapy. Therefore, it is necessary to enable a therapy that is as personalized as possible and to respond promptly to any changes, whether with noticeable symptoms or symptomless.

Automated Analysis of the Two-Minute Walk Test in Clinical Practice Using Accelerometer Data.

One of the core problems for people with multiple sclerosis (pwMS) is the impairment of their ability to walk, which can be severely restrictive in everyday life. Therefore, monitoring of ambulatory function is of great importance to be able to effectively counteract disease progression. An extensive gait analysis, such as the Dresden protocol for multidimensional walking assessment, covers several facets of walking impairment including a 2-min walk test, in which the distance taken by the patient in two minutes is measured by an odometer.

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

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.

Improving Digital Patient Care: Lessons Learned from Patient-Reported and Expert-Reported Experience Measures for the Clinical Practice of Multidimensional Walking Assessment.

Background: Walking assessment (WA) enables meaningful patient mobility assessment. In this context, patient satisfaction with WA can influence assessment compliance and indirectly affect outcomes. One opportunity to assess patient satisfaction is patient-reported and expert-reported experience measures (PREM).

The Dresden Protocol for Multidimensional Walking Assessment (DMWA) in Clinical Practice.

Walking impairments represent one of the most debilitating symptom areas for people with multiple sclerosis (MS). It is important to detect even slightest walking impairments in order to start and optimize necessary interventions in time to counteract further progression of the disability. For this reason, a regular monitoring through gait analysis is highly necessary.

Fear of falling and falls in people with multiple sclerosis: A literature review.

Introduction: Fear of falling (FOF) is a widespread problem affecting about 60% of people with multiple sclerosis (pwMS). Inflammatory lesions in the brain that are caused by the disease result in gait deficits and increase the risk of fall. Falls induce fear of falling and trigger a vicious circle, which in turn increases the likelihood of falling.

Visual Feedback and Postural Control in Multiple Sclerosis.

As people with multiple sclerosis (pwMS) manifest heterogeneous demyelinating lesions that could affect somatosensory or vestibular ways, visual stimulus as feedback could be especially relevant to achieve postural control. This has clinical importance for the development of preventive measures and rehabilitation therapies in order to avoid falls and accidents in this group. In our study, we objectively evaluated the influence of visual feedback on the stabilization of balance in pwMS versus healthy controls (HC) and its potential utility in clinical evaluation.

Balance Testing in Multiple Sclerosis-Improving Neurological Assessment With Static Posturography?

Balance problems can severely limit the quality of life for people with Multiple Sclerosis (pwMS) already in the early stages of the disease. PwMS are usually assessed with the Expanded Disability Status Scale (EDSS), which includes a Romberg test for assessing balance. As the EDSS assessments are subjective to the examining neurologist, the postural stability of pwMS could be objectively quantified by implementing static posturography to detect balance problems and address preventive medical care.