Estimating Biological Stiffness Without Relying on External Joint Perturbations: A Musculoskeletal Modeling Framework
Bibliographic record
Abstract
Understanding the stiffness of joints, skeletal muscles, and tendons is crucial for allowing effective interaction with the environment and facilitating versatile movements. For instance, navigating a slippery surface requires increased stiffness achieved through muscle co-contraction compared to walking on a dry flat surface. Investigating the modulation of biological stiffness in vivo during movement offers valuable insights into motor control strategies, with potential applications in developing control mechanisms for biomimetic prostheses or exoskeletons. Additionally, estimating biological stiffness in clinical settings can aid in identifying biomechanical factors contributing to movement disorders, leading to advancements in neurorehabilitation. However, quantifying how humans modulate biological stiffness in vivo remains challenging due to the complex nature of the neuromusculoskeletal system. The gold standard for estimating joint stiffness involves controlled experiments where joint angles and torques are measured while a robotic manipulator applies small rotations to the joint. This method, while effective, has limitations in terms of lengthy lab-constrained measurements and the need for external joint perturbations, hindering clinical translation. To address these challenges, this thesis focuses on developing an electromyography (EMG)-driven musculoskeletal modeling framework based on the Hill-type muscle model. The framework aims to estimate biological stiffness across joint, muscle, and tendon levels without external perturbations, paving the way for a broader range of movement studies. Validation against experimental data during dynamic ankle rotations demonstrates the framework's ability to calibrate parameters and estimate joint torque and stiffness profiles, even during natural or unperturbed movement. Further validation across anatomical levels reveals accurate estimations of reference data, emphasizing the potential for understanding stiffness at multiple levels. This thesis also explores a hybrid muscle stiffness formulation valid for both dynamic and isometric contractions, enhancing overall estimations and suggesting applications in assistive device control. In conclusion, the proposed framework lays the foundation for estimating biological stiffness during dynamic movement without external perturbations, with future work focusing on refining the model to control assistive devices and for clinical application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".