ArticleSensors (Basel, Switzerland)2019
An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise.
Article 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 35 papers.
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
35 citing papers in PubMed, 99 citations in OpenAlex.
- Including Eye Movement in the Assessment of Physical Fatigue Under Different Loading Types and Road Slopes.Journal of eye movement research · 2026Article
- Smart patches for healthcare industry: a review of emerging technologies, challenges, and developmental opportunities.Biomedical engineering online · 2026Review
- Biomechanical Monitoring of Exercise Fatigue Using Wearable Devices: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- A Wearable System for Knee Osteoarthritis: Based on Multimodal Physiological Signal Assessment and Intelligent Rehabilitation.Sensors (Basel, Switzerland) · 2025Article
- Multiple Organ Phenotype of Fatigue.Biomolecules · 2025Review
- Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle.Scientific reports · 2025Article
- Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training.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
- From Tissue to System: What Constitutes an Appropriate Response to Loading?Sports medicine (Auckland, N.Z.) · 2025Review
- Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System.Sensors (Basel, Switzerland) · 2024Article
- Reliability and Convergent Validity of Endurance Indices Derived from Near-Infrared Spectroscopy and Electromyography during a Bilateral Hanging Task in Amateur Rock Climbers.Journal of functional morphology and kinesiology · 2024Article
- Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.Scientific reports · 2024Article
- E-Textiles for Sports and Fitness Sensing: Current State, Challenges, and Future Opportunities.Sensors (Basel, Switzerland) · 2024Review
- Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.Sensors (Basel, Switzerland) · 2024Article
- Improving wheelchair user sitting posture to alleviate lumbar fatigue: a study utilizing sEMG and pressure sensors.Frontiers in neuroscience · 2024Article
- Microfluidic Wearable Devices for Sports Applications.Micromachines · 2023Review
- Signal Quality Analysis of Single-Arm Electrocardiography.Sensors (Basel, Switzerland) · 2023Article
- Torque Measurement and Control for Electric-Assisted Bike Considering Different External Load Conditions.Sensors (Basel, Switzerland) · 2023Article
- Using Ballistocardiogram and Impedance Plethysmogram for Minimal Contact Measurement of Blood Pressure Based on a Body Weight-Fat Scale.Sensors (Basel, Switzerland) · 2023Article
- Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis.PloS one · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants' muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.
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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.