MotionTherapy@Home – First results of a clinical study with a novel robotic device for automated locomotion therapy at home
Bibliographic record
Abstract
In incomplete spinal cord injured subjects, task-oriented training regimes are applied for enhancement of neuroplasticity to improve gait capacity. However, a sufficient training intensity can only be achieved during the inpatient phase, which is getting shorter and shorter due to economic restrictions. In the clinical environment, complex and expensive robotic devices have been introduced to maintain the duration and the intensity of the training, but up to now only a few exist for continuation of automated locomotion training at home. For continuation of the automated locomotion training at home prototypes of the compact, pneumatically driven orthosis MoreGait have been realized, which generate the key afferent stimuli for activation of the spinal gait pattern generator. Artificial pneumatic muscles with excellent weight-to-force ratio and safety characteristics have been integrated as joint actuators. Additionally, a Stimulative Shoe for generation of the appropriate foot loading pattern has been developed without the need for verticalization of the user. The first results of the pilot study in eight chronic incomplete spinal cord injured subjects indicate that the home-based therapy is safe and feasible. The therapy related improvements of the walking capacity are in the range of locomotion robots used in clinical settings.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".