ArticleMedical & biological engineering & computing2019
Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury.
Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Assessment of muscle activity using electrical stimulation and mechanomyography: a systematic review.Biomedical engineering online · 2021Pooled it
- Feasibility of mechanomyography-based fatigue classification for passive lower-limb exoskeleton evaluation: A pilot study.PloS one · 2026Article
- Study on Muscle Fatigue Classification for Manual Lifting by Fusing sEMG and MMG Signals.Sensors (Basel, Switzerland) · 2025Article
- Characteristics of Post-Exercise Lower Limb Muscle Tremor Among Speed Skaters.Sensors (Basel, Switzerland) · 2025Article
- Effectiveness of Using a Digital Wearable Plantar Pressure Device to Detect Muscle Fatigue: Within-Subject, Repeated Measures Experimental Design.JMIR human factors · 2025Article
- Research on the recognition model of exercise fatigue based on the fusion of sEMG and ECG signals.iScience · 2024Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR.Sensors (Basel, Switzerland) · 2022Article
- Fatigue effect on cross-talk in mechanomyography signals of extensor and flexor forearm muscles during maximal voluntary isometric contractions.Journal of musculoskeletal & neuronal interactions · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity. Graphical abstract ᅟ.
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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.