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Motion Control Using EMG Signals

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 457

Special Issue Editors


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Guest Editor
Faculty of Medicine, Health and Sports, Department of Rehabilitation, Universidad Europea de Madrid, Madrid, Spain
Interests: public health; research methodology in health sciences; injury prevention; rehabilitation

E-Mail Website
Guest Editor
Faculty of Medicine, Health and Sports, Department of Rehabilitation, Universidad Europea de Madrid, Madrid, Spain
Interests: biomechanics; motor control; postural control; balance; EMG; synergies; running performance and injury prevention

Special Issue Information

Dear Colleagues,

Electromyography (EMG) has become a fundamental tool for understanding muscle activation and its role in motor control. By analyzing EMG signals, researchers and clinicians can gain invaluable insights into how the neuromuscular system governs movement during various exercises and activities. This knowledge is crucial for evaluating motor control, preventing injuries, diagnosing musculoskeletal disorders, and improving prognostic and rehabilitation strategies. We are pleased to invite you to submit your research to this Special Issue titled "Motion Control Using EMG Signals". The scope of this Special Issue focuses on the use of EMG to explore muscle activation patterns and motor control across different movements and exercises. Contributions that aim to deepen our understanding of neuromuscular function and its implications for injury prevention and recovery are highly encouraged. The topics of interest for this Special Issue include, but are not limited to, the following:

  • Novel techniques in EMG signal acquisition and analysis.
  • Applications of EMG in motor control and postural control research.
  • Using EMG to evaluate running biomechanics and performance.
  • Injury prevention and rehabilitation strategies informed by EMG data.
  • Diagnosing and monitoring musculoskeletal conditions using EMG.
  • Integration of EMG analysis with wearable technologies and real-time feedback systems. We welcome original research articles, reviews, and methodological studies that contribute to this growing field. Your work will help shape future approaches to using EMG as a tool for enhancing clinical applications, sports performance, and functional rehabilitation. We look forward to receiving your contributions to this exciting Special Issue.

Sincerely,

Prof. Dr. Maria José Giménez-Mestre
Prof. Dr. María García Arrabé
Guest Editors

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Keywords

  • electromyography (EMG)
  • motor control
  • muscle activation
  • injury prevention
  • rehabilitation
  • biomechanics
  • running analysis
  • postural control
  • neuromuscular function

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Published Papers (1 paper)

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Research

18 pages, 5099 KiB  
Article
Surface Electromyographic Features for Severity Classification in Facial Palsy: Insights from a German Cohort and Implications for Future Biofeedback Use
by Ibrahim Manzoor, Aryana Popescu, Alexia Stark, Mykola Gorbachuk, Aldo Spolaore, Marcos Tatagiba, Georgios Naros and Kathrin Machetanz
Sensors 2025, 25(9), 2949; https://doi.org/10.3390/s25092949 - 7 May 2025
Viewed by 316
Abstract
Facial palsy (FP) significantly impacts patients’ quality of life. The accurate classification of FP severity is crucial for personalized treatment planning. Additionally, electromyographic (EMG)-based biofeedback shows promising results in improving recovery outcomes. This prospective study aims to identify EMG time series features that [...] Read more.
Facial palsy (FP) significantly impacts patients’ quality of life. The accurate classification of FP severity is crucial for personalized treatment planning. Additionally, electromyographic (EMG)-based biofeedback shows promising results in improving recovery outcomes. This prospective study aims to identify EMG time series features that can both classify FP and facilitate biofeedback. Therefore, it investigated surface EMG in FP patients and healthy controls during three different facial movements. Repeated-measures ANOVAs (rmANOVA) were conducted to examine the effects of MOTION (move/rest), SIDE (healthy/lesioned) and the House–Brackmann score (HB), across 20 distinct EMG parameters. Correlation analysis was performed between HB and the asymmetry index of EMG parameters, complemented by Fisher score calculations to assess feature relevance in distinguishing between HB levels. Overall, 55 subjects (51.2 ± 14.73 years, 35 female) were included in the study. RmANOVAs revealed a highly significant effect of MOTION across almost all movement types (p < 0.001). Integrating the findings from rmANOVA, the correlation analysis and Fisher score analysis, at least 5/20 EMG parameters were determined to be robust indicators for assessing the degree of paresis and guiding biofeedback. This study demonstrates that EMG can reliably determine severity and guide effective biofeedback in FP, and in severe cases. Our findings support the integration of EMG into personalized rehabilitation strategies. However, further studies are mandatory to improve recovery outcomes. Full article
(This article belongs to the Special Issue Motion Control Using EMG Signals)
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