A BIOFIDELIC MOCK RESIDUAL LIMB FOR PROSTHETIC SOCKET TESTING
Bibliographic record
Abstract
BACKGROUND: Evaluating prosthetic socket fit and function relies on accurately simulating load transfer between the residual limb and the socket. This limb can be either real (of a study participant) or a mock residual limb that simulates in vivo properties. Mock limbs minimize reliance on resource-intensive clinical trials; however, most are static in size, limiting their use in testing clinical outcomes like socket adjustability. OBJECTIVE: To design and validate a biofidelic mock limb, capable of real-time, controllable volume adjustments of up to ±5% limb volume. METHODOLOGY: Water-filled bladders were embedded within a transtibial residual limb model made of a dual-durometer urethane composition, mimicking deep and soft tissue. An Arduino-controlled syringe system was used to actuate volume adjustments. The method was validated through repeatability tests at different rates of volume change, cycling through expansion, holding at maximum volume, and contraction. Volume change was quantified by measuring interfacial pressures between the limb and a static socket. FINDINGS: The limb was fabricated with readily available materials for less than CAD 400. Volume change rate had minimal effect on interfacial pressure throughout the testing cycle, and minimal hysteresis was found between expansion and contraction periods. Repeatability was high, with a coefficient of variation of normalized pressure remaining below 10.4% over three repeated tests. CONCLUSION: The proposed biofidelic limb was validated for its ability to mimic volume change in a transtibial residual limb. The design enables easy replication or customization to simulate different limb physiologies and anatomies. The limb allows for controllable bench-top testing during prototyping of adjustable sockets or other devices, thus bringing devices to clinical use sooner. Layman's Abstract For individuals with a lower-limb amputation, their prosthetic socket—the component that connects their residual limb to the prosthesis—needs to fit well to be comfortable and to function properly. However, the size of a residual limb can change throughout the day due to factors like activity and diet, which can affect the socket fit. Evaluating how prosthetic sockets adapt to residual limb volume changes is important, but doing so through clinical testing with human participants requires extensive resources and involves test conditions that are hard to control. A “mock” residual limb was introduced that simulated real-time, controllable limb volume changes, reducing the need for amputee participants in prosthetic socket testing. The mock limb used water-filled bladders within a soft urethane composition, and the bladder size was controlled by a motorized syringe system, allowing it to expand or contract by up to 5%. The limb’s ability to reliably repeat its volume changes was validated. This tool can help researchers quickly test adjustable prosthetic sockets and other wearable devices in a lab setting, thereby accelerating device development and reducing dependence on clinical testing. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/45759/34271 How To Cite: Phillips C, Nagpal A, Azhari F. A biofidelic mock residual limb for prosthetic socket testing. Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 2, No. 1. DOI:10.33137/cpoj.v8i1.45759 Corresponding Author: Fae Azhari, PhDAffiliation: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada.E-Mail: fae.azhari@utoronto.caORCID ID: https://orcid.org/0000-0003-2559-8566
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".