Finite Element Modelling of Residual Limb and Transtibial Prosthesis Socket to Predict Interfacial Pressure Considering Muscle Contraction
Bibliographic record
Abstract
A lower-limb prosthesis is a mechanical device that replaces part of a biological limb to restore mobility. Approximately 7,400 lower-limb amputation surgeries are performed in Canada each year. The tissues in the residual limb (the "residuum") experience atypical stresses. Importantly, the distal end of the residuum must bear the load that would typically be borne by the foot. To avoid pain and tissue damage, the part of the prosthesis that interfaces with the residuum (the "socket") must be designed to fit properly not only at rest but also during walking and other daily activities. The shape of the residuum changes during movement as muscles contract, which can affect the fit of the socket. To study the residuum-socket interface, we first developed a muscle material model that is able to contract, demonstrates the "force enhancement" behaviour observed during active stretch experiments, and is more numerically stable than existing muscle material models. We implemented our muscle material model using an approximation of the elasticity tensor, which simplified the implementation without sacrificing numerical accuracy. We then created a finite element model of the residuum-socket interface using our muscle material model to evaluate the effect of muscle contraction on the interfacial pressure. During gait, the interfacial pressure at heel strike and toe off were greater at key regions of the residuum when the gastrocnemius muscle was active rather than passive. This difference in pressure distribution could influence analyses of prosthesis design and fit. This thesis advances muscle constitutive modelling by providing a verified approximation of the elasticity tensor applied to muscle models, and by providing one of the few muscle material models available in the literature that exhibits force enhancement and is suitable for simulating active stretching. Our muscle material model implementation has been made publicly available so that others can reproduce and extend our results. This work also advances the modelling of the residuum-socket interface as it is the first finite element modelling study to consider the effect of muscle contraction on residuum shape and socket fit. Our results provide insight into how muscle contraction affects socket fit throughout the gait cycle, potentially leading to improvements in the design and manufacture of prostheses.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".