Soft robotics–inspired sensing system for detecting downward movement and pistoning in prosthetic sockets: A proof-of-concept study
Bibliographic record
Abstract
BACKGROUND: Vertical displacement of the residual limb within transtibial prosthetic socket, often known as "pistoning" or downward movement, may lead to skin breakdowns and ulcers. Downward movement is particularly difficult to self-manage for diabetic individuals living with amputation because of diminished sensation in the residual limb from peripheral neuropathy. Therefore, a customizable sensor at the distal end that can alert the users when high-risk downward movement and pistoning occurs is urgently needed. OBJECTIVES: Presented herein for the first time is a lightweight, inexpensive sensing system inspired by soft robotics that can detect the occurrence and severity of downward movement at the distal end. METHODS: The sensing system consists of a multilayered torus-shaped balloon, allowing easy integration with pin-lock socket systems. The design allows sensing of vertical displacement without imparting high reaction forces back onto the distal end. A benchtop compression tester was used to characterize system performance. Systematic and parametric benchtop tests were conducted to examine the sensor's physical characteristics. Long-term (24-h) stability of the sensor was also recorded. RESULTS: Compared with water, air was determined to be a better medium with a higher linear full-scale span (FSS) because of its compressible nature. Repeatable 0.5-mm vertical displacements yielded a linear (>0.99 R 2 ) FSS of 4.5 mm and a sensitivity of 0.8 kPa/mm. The sensing system is highly precise, with as low as 1% FSS total error band and average hysteresis of 2.84% of FSS. Over 24 h, a 4% FSS drift was observed. CONCLUSION: Sensing system characteristics, coupled with low-cost, customizable fabrication, indicates promising performance for daily use to notify and alert transtibial prosthetic users of downward movement and/or pistoning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".