ArticleFrontiers in neurology2021
Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders.
Article in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 54 citations in OpenAlex.
- Altered Neuromuscular Time-Frequency Organization During Sit-to-Walk in Parkinson's Disease: An Explainable Artificial Intelligence Approach Using Surface Electromyography.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning Aided Kinematic Profiling of Reaching Movements Separates Spinocerebellar Ataxia type 12 and Essential Tremor.Cerebellum (London, England) · 2026Article
- Surface Electromyography for Parkinson's Disease Monitoring: A Review of Machine and Deep Learning Techniques.Sensors (Basel, Switzerland) · 2026Review
- Autoencoder-Enhanced Convolutional Neural Networks for Plantar Pressure-Based Gait Pattern Recognition: Model Development and Cross-Validated Evaluation Study.JMIR formative research · 2026Article
- AI-based wearable system for fall risk prediction in older adults using sEMG and plantar pressure data.Frontiers in bioengineering and biotechnology · 2026Article
- Evaluation of Parkinson's Disease Motor Symptoms via Wearable Inertial Measurements Units and Surface Electromyography Sensors.Bioengineering (Basel, Switzerland) · 2025Article
- Precision Through Detail: Radiomics and Windowing Techniques as Key for Detecting Dens Axis Fractures in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Automated UPDRS Gait Scoring Using Wearable Sensor Fusion and Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors.Sensors (Basel, Switzerland) · 2024Article
- Gait Analysis in Neurorehabilitation: From Research to Clinical Practice.Bioengineering (Basel, Switzerland) · 2023Review
- A Beta Version of an Application Based on Computer Vision for the Assessment of Knee Valgus Angle: A Validity and Reliability Study.Healthcare (Basel, Switzerland) · 2023Article
- Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue?Sensors (Basel, Switzerland) · 2023Article
- Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction.Sensors (Basel, Switzerland) · 2022Article
- GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force.PPAR research · 2022Article
- The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion.Sensors (Basel, Switzerland) · 2021Article
- Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults.Sensors (Basel, Switzerland) · 2021Article
- Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review.Digital healthReview
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (
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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.