Modern technology assists disabled competitors: the first “Cybathlon” special competition in Zürich
Bibliographic record
Abstract
PURPOSE: The purpose of the study was presentation of modern bioengineering technology in order to help people with severe disabilities. METHODS: Bioengineering industry can offer severely disabled people several devices in order to enable them to take part in the competition different than Paralympics. The first international competition for people with disabilities supported by modern assistive technology, such as sensors, motors, displays were allowed to compete in Cybathlon held in Zürich in 2016. About 70 athletes and their teams from 25 countries appeared at the event. RESULTS: There were six disciplines (races): 1) Powered Arms (Upper Extremities) Prostheses Race, 2) Powered Legs (Lower Extremities) Prostheses Race, 3) Powered Wheelchair Race, 4) Powered Exoskeleton Race, 5) Functional Electrical Stimulation Bike Race, 6) Brain-Computer Interface Race. About a quarter of the teams represented industry and the rest represented university laboratories. CONCLUSIONS: The competition was a success. The organisers have decided for it to be organized every four years, just like the Olympic Games for able bodied competitors. The main inventor of the event professor Robert Riener from Zürich Polytechnic (ETHZ) said assistive technology should: a) be user-friendly b) to function well, c) be affordable, d) to be used within the barrier-free environment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".