METHODOLOGY TO INVESTIGATE EFFECT OF PROSTHETIC INTERFACE DESIGN ON RESIDUAL LIMB SOFT TISSUE DEFORMATION
Bibliographic record
Abstract
BACKGROUND: Residual limb discomfort and injury is a common experience for people living with lower limb amputation. Frequently, inadequate load distribution between the prosthetic device and the residual limb is the root cause of this issue. To advance our understanding of prosthetic interface fit, tools are needed to evaluate the mechanical interaction at the prosthetic interface, allowing interface designs to be evaluated and optimised. OBJECTIVES: Present a methodology report designed to facilitate comprehension of the mechanical interaction between the prosthetic interface and the residual limb. As a pilot study, this methodology is used to compare a hands-on and hands-off interface for a single transtibial prosthesis user using secondary Magnetic Resonance Imaging (MRI) data. METHODOLOGY: MRI data of the residual limb while wearing a prosthetic interface is segmented into a hard tissue and a skin surface model. These models are exported as stereolithography (STL) files. Two methods are used to analyse the interface designs. Firstly, CloudCompare software is used to compute the nearest vertex on the skin surface for every vertex on the compiled internal bony surface for both interface types. Secondly, CloudCompare software is used to compare registered skin surfaces of the residual limb while wearing the hands-on and hands-off interfaces. FINDINGS: The maximum and minimum nearest distances between the internal bony surface and skin surface were similar between interface types. However, the distribution of nearest distances was different. When comparing the skin surface while wearing both interfaces, where the fit is more compressive can be visualized. For the dataset used in this study, the classic features of a hands-on Patella Tendon Bearing interface and hands-off pressure cast interface could be identified. CONCLUSION: The methodology presented in this report may give researchers a further tool to better understand how interface designs affect the soft tissues of the residual limb. Layman's Abstract If a person loses all or part of their leg because of injury or disease, they may use a replacement limb to help them walk again. The replacement limb is attached to their remaining leg using a rigid shell and flexible liner. Sometimes, the skin, muscles, and other tissues of their remaining leg are damaged while wearing the replacement limb. Often, this is because the replacement limb fits poorly to their remaining leg. In order to design replacement limbs that do not cause injury, a better understanding of replacement limb fit is required. In this study, a method to understand how replacement limbs deform remaining limb skin, muscles, and other tissues, is presented. 3D medical images are taken of a person’s remaining leg while they wear two different types of replacement limbs. These images are processed into 3D models and then analysed to investigate deformation. Firstly, deformation of the remaining leg, caused by the replacement limb, is calculated based on the distance between the bone surface and skin surface of the remaining leg. Secondly, the shape of the outer surface of the remaining leg while wearing the different replacement limbs is compared. When this method was used to compare the remaining leg of a person while wearing two different types of replacement limb, using previously acquired medical 3D images, differences in the shape of the remaining limb were found. This method may be useful to help design better replacement limbs that do not cause injury in the future. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/42196/32357 How To Cite: Arnstein T, Buis A. Methodology to investigate effect of prosthetic interface design on residual limb soft tissue deformation. Canadian Prosthetics & Orthotics Journal. 2023; Volume 6, Issue 1, No.7. https://doi.org/10.33137/cpoj.v6i1.42196 Corresponding Author: Arjan Buis, PhD Department of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, Scotland.E-Mail: arjan.buis@strath.ac.ukORCID ID: https://orcid.org/0000-0003-3947-293X
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".