ArticleSensors (Basel, Switzerland)2019
Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?
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 25 papers.
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Who cites it
25 citing papers in PubMed.
- Real-Time Wearable sEMG Onset Detection and Phase Discrimination of Sit-to-Stand Movement via a Compact Dual-Channel DD-CNN.Sensors (Basel, Switzerland) · 2026Article
- Article
- Review
- Study on Muscle Fatigue Classification for Manual Lifting by Fusing sEMG and MMG Signals.Sensors (Basel, Switzerland) · 2025Article
- The Impact of Normalization Procedures on Surface Electromyography (sEMG) Data Integrity: A Study of Bicep and Tricep Muscle Signal Analysis.Sensors (Basel, Switzerland) · 2025Article
- Are Surface Electromyography Parameters Indicative of Post-Activation Potentiation/Post-Activation Performance Enhancement, in Terms of Twitch Potentiation and Voluntary Performance? A Systematic Review.Journal of functional morphology and kinesiology · 2024Review
- Stretchable, Flexible, Breathable, Self-Adhesive Epidermal Hand sEMG Sensor System.Bioengineering (Basel, Switzerland) · 2024Article
- Comparative electromyography analysis of subphase gait disorder in chronic stroke survivors.PeerJ · 2024Article
- A comparison of individual force decline profiles during a fatiguing eccentric trunk flexion and extension protocol: a pilot study.Frontiers in sports and active living · 2024Article
- A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform.Sensors (Basel, Switzerland) · 2023Article
- Development of a Low-Cost Portable EMG for Measuring the Muscular Activity of Workers in the Field.Sensors (Basel, Switzerland) · 2023Article
- Design Validation of a Low-Cost EMG Sensor Compared to a Commercial-Based System for Measuring Muscle Activity and Fatigue.Sensors (Basel, Switzerland) · 2022Article
- Improved Multichannel Electromyograph Using Off-the-Shelf Components for Education and Research.Sensors (Basel, Switzerland) · 2022Article
- Classifying Muscle States with One-Dimensional Radio-Frequency Signals from Single Element Ultrasound Transducers.Sensors (Basel, Switzerland) · 2022Article
- Proposed Fatigue Index for the Objective Detection of Muscle Fatigue Using Surface Electromyography and a Double-Step Binary Classifier.Sensors (Basel, Switzerland) · 2022Article
- Measuring Biosignals with Single Circuit Boards.Bioengineering (Basel, Switzerland) · 2022Review
- Design and Evaluation of a Custom-Made Electromyographic Biofeedback System for Facial Rehabilitation.Frontiers in neuroscience · 2022Article
- Pre-exhaustion Training, a Narrative Review of the Acute Responses and Chronic Adaptations.International journal of exercise science · 2022Article
- Surface Electromyography Study Using a Low-Cost System: Are There Neck Muscles Differences When the Passenger Is Warned during an Emergency Braking Inside an Autonomous Vehicle?Sensors (Basel, Switzerland) · 2021Article
- Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions.
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