Air microfluidics-enabled soft robotic transtibial prosthesis socket liner toward dynamic management of residual limb contact pressure and volume fluctuation
Bibliographic record
Abstract
Residual limb volume fluctuation and the resulting contact pressures are some of the key factors leading to skin ulcerations, suboptimal prosthetic functioning, pain, and diminishing quality of life of transtibial amputees. Self-management of socket fit is complicated by peripheral neuropathy, reducing the perception of pressure and pain in the residual limb. We introduce a novel proof-of-concept for a transtibial prosthetic socket liner with the potential to dynamically adjust the fit between the limb and socket. The core of the technology is a small air microfluidic chip (10 cm3 and 10 g) with 10 on-chip valves that enable sequential pressurizing of 10 actuators in custom sizes to match the pressures required by the residual limb's unique anatomy. The microfluidic chip largely reduced the number of electromechanical solenoid valves needed for sequential control of 10 actuators (2 instead of 10 valves), resulting in the reduction of the required power, size, mass, and cost of the control box toward an affordable and wearable prosthetic socket. Proof-of-concept testing demonstrated that the applied pressures can be varied in the desired sequence and to redistribute pressure. Future work will focus on integrating the system with biofidelic prosthetic sockets and residual limb models to investigate the ability to redistribute pressure away from pressure-sensitive regions (e.g., fibular head) to pressure tolerant areas. Overall, the dynamic prosthesis socket liner is very encouraging for creating a dynamic socket fit system that can be seamlessly integrated with existing socket fabrication methods for managing residual limb volume fluctuations and contact pressure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".