ArticleScientific reports2025
Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis 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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of breathing training on walking ability and quality of life in patients with multiple sclerosis: systematic review and meta-analysis of randomized controlled trials.Frontiers in immunology · 2025Pooled it
- Toward Personalized Rotator Cuff Physical Therapy Dosage Using a Machine Learning-Based Pilot Study with EMG.Bioengineering (Basel, Switzerland) · 2026Article
- IFIANet: Frequency Attention Network for Time-Frequency in sEMG-Based Motion Intent Recognition.Sensors (Basel, Switzerland) · 2025Article
- Evaluating Robotic Walker Performance: Stability, Responsiveness, and Accuracy in User Movement Detection.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neuromuscular diseases. Its main recording techniques include video cameras and wearable sensors. However, the present methods involve measuring surface electromyograms (sEMGs) to analyze muscle activities. The primary goal of this study is to estimate gait parameters under different power capacity of muscle by sEMGs measured from lower limbs. A self-made wireless device recorded sEMGs from two muscles of each foot, and GaitUp Physilog
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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.