@article{4694,
  author = {Tuan Nguyen Minh},
  title = {Design and Validation of an AI-Integrated Neuromuscular Assessment System for Physical Fitness Evaluation},
  journal = {Journal of Information Organization},
  year = {2026},
  volume = {16},
  number = {1},
  doi = {https://doi.org/10.6025/jio/2026/16/1/13-25},
  url = {https://www.dline.info/jio/fulltext/v16n1/jiov16n1_2.pdf},
  abstract = {This study presents the design and validation of an AI-integrated neuromuscular assessment system for
objective evaluation of physical fitness, addressing the limitations of subjective clinical scales in rehabilitation
and athletic training contexts. The proposed framework employs a four layer modular architecture: (1)
synchronized multimodal data acquisition using high density EMG arrays (8–16 channels), force plates, and
inertial measurement units; (2) adaptive feature engineering with EMG Root Mean Square (RMS) as the
primary biomarker; (3) a hybrid AI evaluation engine combining regression and classification heads; and
(4) an intelligent feedback interface delivering tier based performance grading (A–E). Validation was
conducted through a 40 day longitudinal training study with 500 simulation runs. Results demonstrated a
strong positive correlation (r = 0.87, p < 0.001) between EMG RMS values and training duration, confirming
RMS as a sensitive indicator of neuromuscular adaptation. The AI model achieved exceptional performance
metrics, including 96.2% evaluation accuracy, 0.93 test retest reliability, and rapid convergence (MSE <
0.0015 by iteration 9) with minimal overfitting across training, validation, and test datasets. Mechanical
parameters peak force (215 ± 28 N), average force (168 ± 21 N), joint angular velocity (3.42 ± 0.38 rad/s),
and power output (412 ± 45 W) corroborated EMG derived physiological improvements. The system’s
robustness was further validated through tightly bounded error distributions (mean test error: 0.0016 ±
0.0006) across repeated simulations. By transforming raw physiological signals into interpretable fitness
scores with clinical grade reliability, this framework establishes a foundation for precision rehabilitation,
personalized training optimization, and objective monitoring of neuromuscular function in both clinical and
athletic settings.},
}