Kinematic and Kinetic Assessment of a Novel Polycentric Knee Joint for Powered Walking Assist Exoskeletons
Bibliographic record
Abstract
Loss of mobility and independence directly affects the quality of life of many vulnerable individuals. In order to address this, researchers have been developing wearable walking assist exoskeletons to aid users with their daily activities. While this technology has advanced tremendously in the past decade, current exoskeletons are yet to be ergonomic, causing discomfort and injuries to the user, leading to early device rejection. This research intends to assess the kinematic and kinetic compatibility of a novel knee joint suitable for exoskeletons. The proposed knee design can be adapted to accommodate a kinematic offset and optimize force delivery. This is achieved by ensuring that the mechanical and biological joint rotation axes are aligned and that the moment arm can be varied to mimic the mechanical characteristics of a biological knee. Model simulations and mechanical testing of fabricated prototypes were achieved to analyze and validate the design. Results confirmed the compatibility of the design, which demonstrated kinematic absolute error values of 1.68 and 0.32 mm for the offset and aligned joints, respectively. It was shown that the moment arm can be varied throughout flexion, allowing future iterations to achieve optimal and effective moment transfer.
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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) | 0.000 | 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 | 0.001 | 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".