ArticleFrontiers in neurology2020
Surface Electromyography Meets Biomechanics: Correct Interpretation of sEMG-Signals in Neuro-Rehabilitation Needs Biomechanical Input.
Article in Frontiers in neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 34 citations in OpenAlex.
- Review
- A Co-Located sEMG-pFMG Dataset for Hand Gesture Recognition Under Varying Arm-Position Conditions.Sensors (Basel, Switzerland) · 2026Article
- Sensor-Based Assessment of Quadriceps EMG-Amplitude-to-Torque Ratios at Different Knee Angles: An Exploratory Cross-Sectional Study.Sensors (Basel, Switzerland) · 2026Article
- Surface Electromyography-Based Evaluation of Neuromuscular Recruitment Under Different Resistance Conditions During Water-Resistance Rowing.Sensors (Basel, Switzerland) · 2026Article
- Comparison of Muscle Activation between Variable Resistance Training and Free Weight Training: A Systematic Review and Meta-Analysis.Journal of human kinetics · 2026Review
- Concurrent HIIT and Resistance Training for Musculoskeletal Function: A Systematic Review of Neuromuscular, Morphological, and Performance Adaptations.Life (Basel, Switzerland) · 2026Review
- Tibialis anterior surface electromyography during ankle dorsiflexion as a complementary assessment measure after stroke.Frontiers in neurology · 2026Observational
- Forearm rotation and elbow angle differentially modulate biceps brachii and brachioradialis muscle stiffness and EMG activity during low-load isometric contractions: a cross-sectional study in healthy individuals.BMC sports science, medicine & rehabilitation · 2025Article
- Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment.Diagnostics (Basel, Switzerland) · 2025Review
- Neural control meets biomechanics in the motor assessment of neurological disorders: a narrative review.Frontiers in neural circuits · 2025Review
- Spinal muscle characteristics during three different types of locomotion activities among college students with idiopathic scoliosis.BMC musculoskeletal disorders · 2024Article
- Surface Electromyography Data Analysis for Evaluation of Physical Exercise Habits between Athletes and Non-Athletes during Indoor Rowing.Sensors (Basel, Switzerland) · 2024Article
- Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization.Bioengineering (Basel, Switzerland) · 2024Article
- Kinematic and Neuromuscular Measures of Intensity During Drop Jumps in Female Volleyball Players.Frontiers in psychology · 2021Article
- Introduction of a sEMG Sensor System for Autonomous Use by Inexperienced Users.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coordinated activation of muscles is the basis for human locomotion. Impaired muscular activation is related to poor movement performance and disability. To restore movement performance, information about the subject's individual muscular activation is of high relevance. Surface electromyography (sEMG) allows the pain-free assessment of muscular activation and many ready-to-use technologies are available. They enable the usage of sEMG measurements in several applications. However, due to the fact that in most rehabilitation applications dynamic conditions are analyzed, the correct interpretation of sEMG signals remains difficult which hinders the spread of sEMG in clinical applications. From biomechanics it is well-known that the sEMG signal depends on muscle fiber length, contraction velocity, contraction type and on the muscle's biomechanical moment. In non-isometric conditions these biomechanical factors have to be considered when analyzing sEMG signals. Additionally, the central nervous system control strategies used to activate synergistic and antagonistic muscles have to be taken into consideration. These central nervous system activation strategies are rarely known in physiology and are hard to manage in pathology. In this perspective report we discuss how the consideration of biomechanical factors leads to more reliable information extraction from sEMG signals and how the limitations of sEMG can be overcome in dynamic conditions. This is a prerequisite if the use of sEMG in rehabilitation applications is to extend. Examples will be given showing how the integration of biomechanical knowledge into the interpretation of sEMG helps to identify the central nervous system activation strategies involved and leads to relevant clinical information.
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