ArticleBrain sciences2021
Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis.
Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06874764 (Effects of Agility and Cognitive Training With Reaction System on Cognitive Functions and Walking in Individuals With Multiple Sclerosis), which is not on this map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Effects of Agility and Cognitive Training With Reaction System on Cognitive Functions and Walking in Individuals With Multiple Sclerosis
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Artificial Intelligence Applications to Support Physical Activity, Mobility, and Fatigue Management in People with Multiple Sclerosis: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Exploring parameter optimisation in machine learning algorithms for locomotor task discrimination using wearable sensors.Scientific reports · 2025Article
- Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics.Entropy (Basel, Switzerland) · 2024Article
- Consensus quality indicators for monitoring multiple sclerosis.The Lancet regional health. Europe · 2024Review
- Torsobarography: Intra-Observer Reliability Study of a Novel Posture Analysis Based on Pressure Distribution.Sensors (Basel, Switzerland) · 2024Article
- A future of AI-driven personalized care for people with multiple sclerosis.Frontiers in immunology · 2024Article
- NONAN GaitPrint: An IMU gait database of healthy young adults.Scientific data · 2023Article
- Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach.Sensors (Basel, Switzerland) · 2023Article
- 3D Printing of Individual Running Insoles - A Case Study.Orthopedic research and reviews · 2023Article
- A Pilot Study of Plantar Mechanics Distributions and Fatigue Profiles after Running on a Treadmill: Using a Support Vector Machine Algorithm.Journal of healthcare engineering · 2023Article
- Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach.Journal of clinical medicine · 2022Article
- Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway.Biomedical engineering online · 2022Article
- Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis.Frontiers in artificial intelligence · 2022Article
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Automated Analysis of the Two-Minute Walk Test in Clinical Practice Using Accelerometer Data.Brain sciences · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (κ = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (κ = 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.