Soft Dynamic Fluidic Cushion for Pressure Sore Management in Transtibial Prosthetics: A Proof-of-Concept Study
Bibliographic record
Abstract
OBJECTIVE: Due to volume fluctuations, bony prominences contacting prosthetic sockets are susceptible to skin breakdown due to constant pressure and shear stresses from lateral and vertical displacements throughout gait. A low-profile unloading cushion that can be controlled with a millisecond response to dynamically provide localized socket fit at the fibular head may offer stress relief during gait. METHODS: The study presents a low-profile (<0.5 mm thick) dynamic fluidic cushion that can provide targeted cushioning during gait to mitigate high contact pressures at bony prominences while minimizing duration of pressure and shear. The proof-of-concept study comprised benchtop testing using a residual limb model. RESULTS: The dynamic cushion effectively unloaded high pressure caused by the body weights only during the stance phase at the fibular head for up to 80 kg by mitigating and redistributing contact pressures away from the fibular head to the immediately surrounding soft tissues. Results demonstrated that the fibular head peak contact pressure could be reduced by as much as 94% (98 to 6 kPa) with a response time of 250 milliseconds in benchtop tests, fast enough to turn on during stance and off during swing. CONCLUSION: The proposed dynamic fluidic cushion has the potential to offer unloading during gait to prevent skin damage at pressure-sensitive spots, notably benefiting individuals with complex limb geometries. SIGNIFICANCE: We introduce a new method with the potential to be integrated into prosthetist workflows to locally adjust socket fit (e.g., at the fibular head) using elastomer sheets and a handheld heat press.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".