Electronics-Free Soft Robotic Knee Brace for Dynamic Unloading During Gait for Knee Osteoarthritis: A Proof-of-Concept Study
Bibliographic record
Abstract
Abstract We present a novel electronics-free soft robotic knee brace which employs a closed-loop fluidic regenerative (CLFR) system for dynamic unloading in unicompartmental tibiofemoral osteoarthritis (OA). The existing dynamic unloaders are bulky, large, and heavy, and have low compliance likely due to the use of an electrical control box, which is eliminated in the CLFR system. The system consists of a commercial unloading knee brace, a spring-loaded bellow inserted under the heel inside a shoe, a soft-fluidic actuator (bladder), and tubing for fluid transfer. The novelty lies in the fact that the user's body weight (self-powered) compresses the bellow to provide energy to inflate the air bladder placed at the knee. As a result, the yielded pressure unloads the undesirable forces due to knee OA during the stance phase of gait while strategically applying no forces during the swing phase. The knee bladder contact pressure/force, the system response time, and the durability were evaluated via contact pressure measurements for six systems with varying bellow volumes and either pneumatic or hydraulic configurations. All systems produced safe pressure outputs for human skin within a tested bodyweight range of 60–90 kg. Pneumatic and hydraulic systems achieved 250 ms and 400 ms pressurization response times, respectively. During cyclic loading, pneumatic and hydraulic systems demonstrated less than 1% and ∼10% pressure loss, respectively. Overall, the CLFR system created a promising electronics-free solution for dynamically unloading the knee during gait, indicating a potential new paradigm for knee braces.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".