Feedback-controlled robotics-assisted treadmill exercise to assess and influence aerobic capacity early after stroke: a proof-of-concept study
Bibliographic record
Abstract
PURPOSE: The majority of post-stroke individuals suffer from low exercise capacity as a secondary reaction to immobility. The aim of this study was to prove the concept of feedback-controlled robotics-assisted treadmill exercise (RATE) to assess aerobic capacity and guide cardiovascular exercise in severely impaired individuals early after stroke. METHOD: Subjects underwent constant load and incremental exercise testing using a human-in-the-loop feedback system within a robotics-assisted exoskeleton (Lokomat, Hocoma AG, CH). Inclusion criteria were: stroke onset ≤8 weeks, stable medical condition, non-ambulatory status, moderate motor control of the lower limbs and appropriate cognitive function. Outcome measures included oxygen uptake kinetics, peak oxygen uptake (VO2peak), gas exchange threshold (GET), peak heart rate (HRpeak), peak work rate (Ppeak) and accuracy of reaching target work rate (P-RMSE). RESULTS: Three subjects (18-42 d post-stroke) were included. Oxygen uptake kinetics during constant load ranged from 42.0 to 60.2 s. Incremental exercise testing showed: VO2peak range 19.7-28.8 ml/min/kg, GET range 11.6-12.7 ml/min/kg, and HRpeak range 115-161 bpm. Ppeak range was 55.2-110.9 W and P-RMSE range was 3.8-7.5 W. CONCLUSIONS: The concept of feedback-controlled RATE for assessment of aerobic capacity and guidance of cardiovascular exercise is feasible. Further research is warranted to validate the method on a larger scale. IMPLICATIONS FOR REHABILITATION: Aerobic capacity is seriously reduced in post-stroke individuals as a secondary reaction to immobility. Robotics-assisted walking devices may have substantial clinical relevance regarding assessment and improvement of aerobic capacity early after stroke. Feedback-controlled robotics-assisted treadmill exercise represents a new concept for cardiovascular assessment and intervention protocols for severely impaired individuals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".