Exo-supportive device for individuals with restricted mobility
Bibliographic record
Abstract
The main goal of this work is to design and build an economical and efficient mobility-device that is external to the user and is able to improve motor performance by supporting task-specific movement. Increasing numbers of people of all ages are affected by injuries that result in restricted movement. These people are victims of car or other accidents, children with congenital pathologies and others suffering from traumas caused by lifting and carrying heavy loads in various work places. All these situations require a simple and cost-effective solution. There are few existing technologies that allow people to recover from or, potentially, to prevent these serious injuries. The few that do exist at a Technology Readiness Level (TRL) 7 or higher are prohibitively expensive (US$100k-150k). The developed prototype design has four main benefits when compared to similar existing devices. One is the improvement of the gait-cycle allowing the wearer to perform more natural movements. The new design also has more flexible joints to give the wearer more comfort while performing complex movements. The integrated micro-controller and sensors allow the device to operate safely without reacting to unintentional motions (coughing, sneezing and shivering). Another benefit is the scalability of the device: it is adaptable to the unique physiology of any person. The prototype currently has limited operation endurance, in the order of 30 minutes with a single battery and without recharging the energy source. Current research is aimed at designing a more effective energy system to extend operation endurance.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".