A Treadmill and Motion Coupled Virtual Reality System for Gait Training Post-Stroke
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A virtual reality (VR)-based locomotor training system has been developed for gait rehabilitation post-stroke. The system consists of a self-paced treadmill mounted onto a 6-degrees-of-freedom motion platform. Virtual environments (VEs) that are synchronized with the speed of the treadmill and the motions of the platform are rear-projected onto a screen in front of the walking subject. A feasibility study was conducted to test the capability of two stroke patients and one healthy control to be trained with the system. Three VE scenarios (corridor walking, street crossing, and park stroll) were woven into a gait-training program that provided three levels of complexity (walking speed, slopes, collision avoidances), progression criteria (number of successful trials) and knowledge of results. Results show that, with practice, patients can effectively increase their gait speed as demanded by the task and adapt their gait with respect to the change in physical terrain. However, successful completion of tasks requiring adaptation to increasing demands related to speed and physical terrains does not necessarily predict the patient's ability to anticipate and avoid collision with obstacles during walking. This feasibility study demonstrates that persons with stroke are able to adapt to this novel VR system and be immersed in the VEs for gait training.
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.
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 | 0.000 | 0.000 |
| 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 it