UC San Diego

The musculoskeletal (MSK) system, pivotal in human movement, has been a subject of extensive research, leading to valuable insights into the complex interplay of its components. A key challenge in understanding and modeling the MSK system is bridging the gap between micro and continuum length scales of the muscle tissue. Multi-scale models have emerged as a powerful tool for linking these scales, enabling a more comprehensive analysis of the mechanical behavior of muscles.

This dissertation explores recent developments in multi-scale models and their applications to various aspects of the MSK system. These models offer a holistic perspective by connecting microstructural characteristics with macroscale mechanical properties. One significant focus of research involves understanding lateral force transmission within muscles. By incorporating multi-scale modeling, researchers have improved their understanding of how forces are transmitted and distributed within the muscle, shedding light on the mechanisms underlying efficient muscle function.

Furthermore, researchers are investigating the relationships between force, intra-muscular pressure, and strain measures. Multi-scale models provide a valuable framework to explore these correlations, helping to elucidate the intricate connections between mechanical properties and physiological functions. The insights gained from these investigations have profound implications for understanding muscle performance and injury mechanisms.

In recent years, a growing trend involves the integration of physics-informed machine learning techniques for simultaneous motion prediction and MSK parameter identification. Leveraging machine learning, researchers are developing models that can predict muscle motions while simultaneously estimating important MSK parameters, offering a more comprehensive understanding of musculoskeletal biomechanics.

Moreover, Bayesian Parameter Estimation methods have gained popularity for estimating parameters across multiple length scales of the muscle. This approach provides a robust statistical framework for parameter identification, enhancing the accuracy and reliability of model predictions. By incorporating Bayesian techniques, researchers can efficiently estimate parameters from experimental data, improving the overall fidelity of multi-scale models.

In conclusion, multi-scale modeling approaches are revolutionizing our understanding of the musculoskeletal system. By integrating micro and continuum length scales, researchers are advancing our knowledge of lateral force transmission, force-strain relationships, and the dynamics of muscle motion. Additionally, the integration of physics-informed machine learning and Bayesian Parameter Estimation techniques is enhancing our ability to estimate parameters and predict complex motions accurately. These advancements have the potential to revolutionize clinical and biomechanical applications, ultimately leading to better outcomes for patients and athletes alike.