The Neura-Feat Powered Exoskeleton; Design and Control
Bibliographic record
Abstract
Currently between 250,000 and 500,000 people globally suffer a life-changing spinal cord injury (SCI) each year increasing both the morbidity and mortality of those afflicted. The design of robotic exoskeletons to support, protect and enable movement of disabled individuals have been developed for the past 35 years. However, the successful design of a human like exoskeleton which act smoothly based on the person brian orders without external inputs still is a challenge. This paper presents the design of the Neuro-Feat exoskeleton which uses the brain signals through a brain computer interface to control the exoskeleton actions. The goal of this project is to help SCI people with a reliable, helpful and affordable exoskeleton help them in tackling daily life challenges without relying on others. The design requirements and the challenges for Neura-Feat evaluation comply with the regulations of the Cybathlon competition on 2020 in Zurich.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".