Trial reportSensors (Basel, Switzerland)2019
A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation.
Trial report in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Overuse-Related Injuries of the Musculoskeletal System: Systematic Review and Quantitative Synthesis of Injuries, Locations, Risk Factors and Assessment Techniques.Sensors (Basel, Switzerland) · 2021Pooled it
- Fatigue Monitoring Through Wearables: A State-of-the-Art Review.Frontiers in physiology · 2021Pooled it
- Article
- Biomechanical Monitoring of Exercise Fatigue Using Wearable Devices: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- An Energy-Efficient Flexible Multi-Modal Wireless Sweat Sensing System Based on Laser Induced Graphene.Sensors (Basel, Switzerland) · 2023Article
- [Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Heart Rate Variability-Based Subjective Physical Fatigue Assessment.Sensors (Basel, Switzerland) · 2022Article
- Application of Surface Electromyography in Exercise Fatigue: A Review.Frontiers in systems neuroscience · 2022Review
- A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold.Sensors (Basel, Switzerland) · 2021Article
- Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method.Sensors (Basel, Switzerland) · 2021Article
- Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors.Sensors (Basel, Switzerland) · 2020Article
- Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal.Entropy (Basel, Switzerland) · 2020Article
- Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining.Sensors (Basel, Switzerland) · 2020Article
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 2 institutions in 1 country.
Funding
Abstract
Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (
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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.