Musculoskeletal Modeling in Neurorehabilitation and Precision Medicine: Enhancing Accuracy, Personalization, and Clinical Applicability

Musculoskeletal modeling has become an important tool in computational biomechanics, advancing precision medicine, movement pathology assessment, and the development of assistive technologies. By enabling estimation of muscle forces, internal joint dynamics, and neuromuscular control strategies, these frameworks provide critical insights for neurorehabilitation and clinical decision-making. However, transitioning these computational models from laboratory settings to routine clinical practice presents major challenges. Traditional approaches typically rely on generic anatomical scaling that does not reflect an individual’s musculoskeletal geometry, use physiological signals affected by measurement noise and motion artifacts, and require computationally expensive optimization routines that are too slow for clinical use. As a result, these approaches often fail to capture subject-specific functional variability. Recent technological advancements target these specific limitations: sensor fusion combines multiple wearable or imaging modalities to replace generic anatomical scaling with subject-specific geometric calibration, improving personalization accuracy and reducing the impact of signal noise; synergy-assisted modeling simplifies neuromuscular control by exploiting coordinated muscle activation patterns instead of estimating each muscle independently; and machine learning enables faster, subject-specific parameter estimation that can replace costly iterative optimization. Where clinical applications require immediate feedback, such as closed-loop control of assistive devices or interactive rehabilitation training, achieving low-latency model updates becomes important for improving diagnostic accuracy, optimizing human-robot interaction, and bridging the gap between computational biomechanics and translational clinical applications.The primary objective of this Research Topic is to address the gap between complex musculoskeletal modeling frameworks and their practical application in clinical and rehabilitation settings. Current computational models often involve a trade-off between the level of anatomical and physiological detail they represent (for example, the number of muscles, degrees of freedom, and tissue-level parameters) and the computational speed needed for practical use, limiting their utility in personalized healthcare. To overcome these barriers, this collection seeks to highlight recent technological and methodological advances that enhance model personalization and translation, provided such advances are supported by sound experimental validation and demonstrated generalizability across subjects rather than single-case findings alone. Key focus areas include integrating muscle synergy concepts to simplify neuromuscular control, leveraging wearable sensor fusion for dynamic model calibration, and utilizing machine learning algorithms for subject-specific parameter estimation and low-latency predictions, each benchmarked against established methods. By compiling state-of-the-art research on these integrated approaches, this topic aims to establish validated, subject-specific modeling workflows that effectively drive clinical decision-making and optimize assistive device design.To advance personalized computational biomechanics, this Research Topic seeks contributions that demonstrate innovative methodologies, technological integration, and clinical translation, with clear experimental validation and comparison to existing methods. We welcome Original Research, Review, Systematic Review, and Methods articles. Specific themes of interest include, but are not limited to:Subject-specific personalization: Advanced workflows for anatomical scaling, kinematic calibration, force estimation, and muscle-tendon parameter optimization, validated across cohorts rather than isolated cases. Synergy-driven and EMG-assisted modeling: Incorporating neuromuscular control strategies to refine muscle force estimation. Sensor fusion and wearables: Utilizing wearable sensors for real-time model calibration and dynamic simulations. Data-driven and machine learning approaches: Machine learning strategies for subject-specific parameter estimation, fast parameter calibration, EMG-to-force mapping, virtual sensing, and low-latency predictions to enable real-time simulation, with performance validated against ground-truth or established benchmarks. Translational and assistive applications: Deployment of musculoskeletal models in neurorehabilitation, movement disorder assessment (e.g., stroke, cerebral palsy), and patient-specific rehabilitation protocols, excluding trauma injury risk prediction or prevention. Validation and reproducibility: Experimental validation protocols, benchmark datasets, and sensitivity analyses across subject populations.Submissions focused primarily on musculoskeletal trauma, injury risk prediction, or injury prevention fall outside the scope of this Research Topic. Topic editors: Chih-Hsiu Cheng, Rafhael Milanezi De Andrade Source: https://www.frontiersin.org/research-topics/84228/musculoskeletal-modeling-in-neurorehabilitation-and-precision-medicine-enhancing-accuracy-personalization-and-clinical-applicability