ArticleSensors (Basel, Switzerland)2021
Prediction of Myoelectric Biomarkers in Post-Stroke Gait.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- Cellular and Molecular Targets for Non-Invasive, Non-Pharmacological Therapeutic/Rehabilitative Interventions in Acute Ischemic Stroke.International journal of molecular sciences · 2022Pooled it
- A Systematic Review of Diagnostic Accuracy and Clinical Applications of Wearable Movement Sensors for Knee Joint Rehabilitation.Sensors (Basel, Switzerland) · 2021Pooled it
- Domain-Aware Interpretable Machine Learning Model for Predicting Postoperative Hospital Length of Stay from Perioperative Data: A Retrospective Observational Cohort Study.Bioengineering (Basel, Switzerland) · 2026Article
- EMG-Spectrogram-Empowered CNN Stroke-Classifier Model Development.Life (Basel, Switzerland) · 2026Article
- A multimodal machine learning approach to forecast upper limb motor recovery after stroke using kinematic and electromyographic data - A pilot-study.Journal of neuroengineering and rehabilitation · 2026Article
- Analysis of the Temporo-Spatial and Electromyographic Characteristics of Gait in a Hemiplegic Patient: A Single-Subject Case Report.Reports (MDPI) · 2025Article
- InertialMov: Machine Learning Test Based on Inertial Sensors to Predict Mobility Impairment in Low Back Pain Patients.Sensors (Basel, Switzerland) · 2025Article
- Interpretable framework for predicting preoperative cardiorespiratory fitness using wearable data.Computer methods and programs in biomedicine · 2025Article
- A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction.Scientific reports · 2025Article
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- Wearable system for the measurement of gait cycle kinematic and kinetic signals.Wearable technologies · 2025Article
- Quantitative evaluation method of stroke association based on multidimensional gait parameters by using machine learning.Frontiers in neuroinformatics · 2025Article
- Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study.Sensors (Basel, Switzerland) · 2024Observational
- AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.Sensors (Basel, Switzerland) · 2024Article
- Automatic rehabilitation exercise task assessment of stroke patients based on wearable sensors with a lightweight multichannel 1D-CNN model.Scientific reports · 2024Article
- Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics.Entropy (Basel, Switzerland) · 2024Article
- Estimation of patient-reported outcome measures based on features of knee joint muscle co-activation in advanced knee osteoarthritis.Scientific reports · 2024Article
- Interpreting Stroke-Impaired Electromyography Patterns through Explainable Artificial Intelligence.Sensors (Basel, Switzerland) · 2024Article
- Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis.Heliyon · 2024Article
- Machine learning-based gait adaptation dysfunction identification using CMill-based gait data.Frontiers in neurorobotics · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation.
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What OpenQuestion holds
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.