Research on Early Identification and Intervention Techniques for Neuromuscular Function Degeneration
DOI:
https://doi.org/10.70088/e6anh527Keywords:
neuromuscular function, early identification, sports science, physical therapyAbstract
The degeneration of neuromuscular function is a common phenomenon observed in various chronic diseases as well as in the natural aging process. Early identification of such functional decline is critical for implementing timely intervention strategies, improving patients' physical performance, and accelerating rehabilitation outcomes. Traditional assessment methods, which primarily rely on clinical scales and subjective evaluations, are increasingly being complemented or replaced by comprehensive evaluation systems grounded in sports science, biomechanics, and physical therapy methodologies. Advanced techniques such as motion capture analysis, surface electromyography (sEMG), force platform measurements, and proprioceptive assessments have been employed to detect subtle abnormalities in neuromuscular control, delayed muscle activation patterns, and impaired sensorimotor integration. By capturing fine-grained movement and physiological signals, these methods provide a detailed understanding of early neuromuscular dysfunction that is often undetectable through conventional clinical examination. This article systematically examines the early manifestations of neuromuscular functional degradation, summarizes diagnostic frameworks, and analyzes pattern data that reflect underlying neuromotor impairments. By integrating these approaches, the study highlights strategies to enhance diagnostic accuracy, optimize individualized physical therapy interventions, and improve the efficiency of rehabilitation programs. The findings contribute to the theoretical foundation for future development of early intervention methods, offering practical guidance for both clinical assessment and applied therapeutic practices in neuromuscular health management.
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Copyright (c) 2025 Yihao Wang (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.





