Electromyography normalization and assessment methods for muscle activity: A systematic review and meta-analysis

Background: Electromyographic (EMG) normalization is essential for accurately measuring muscle activity across different populations and tasks. Various methods, including Maxi-mum Voluntary Isometric Contraction (MVIC) and Recurrence Quantification Analysis (RQA), have been employed to improve measurement reliability. However, discrepancies in results across studies necessitate a comprehensive review of normalization techniques. Objective: This report analyzed 40 research studies to evaluate EMG normalization and assessment methods, focusing on their reliability, applicability, and effectiveness across healthy and clinical populations. Methods: The studies employed a range of normalization techniques, including MVIC, submaximal con-tractions, task-specific approaches, and advanced methods such as RQA and Pre-Fatigue Maximal Voluntary Contraction (PFMVC). The analysis considered the suitability of each method based on participant characteristics and movement tasks. Results: MVIC demonstrated consistent reliability in healthy populations, whereas dynamic and task-specific normalization methods were advan-tageous in specific research contexts. Clinical studies reported difficulties in performing maximal contractions, suggesting the need for alternative normalization methods for patients. Emerging techniques, such as RQA and PFMVC, required specialized expertise and resources. Conclusion: Method selection should align with study parameters, considering population characteristics and task demands. Researchers highlighted trade-offs between reliability, validity, and feasibility, emphasizing the need for further comparative research to refine EMG normalization practices.