Metabolic Demand and Muscle Activation during Different Forms of Bodyweight Supported Locomotion in Men with Incomplete SCI
Bibliographic record
Abstract
Body weight supported locomotor training uses neuroplasticity principles to improve recovery following a spinal cord injury (SCI). Steady state locomotion using the same body weight support (BWS) percent was compared in 7 males (42.6 ± 4.29 years) with incomplete SCI and matched (gender, age) noninjured controls (42.7 ± 5.4 years) using the Lokomat, Manual Treadmill, and ZeroG. The VO2000, Polar Heart Rate (HR) Monitor, and lower limb electromyography (EMG) electrodes were worn during the 2-minute sessions. Oxygen uptake (VO2) and HR were expressed as percentage of peak values obtained using progressive arm ergometry; VO2 was also expressed relative to resting metabolic equivalents (METS). Filtered EMG signals from tibialis anterior (TA), rectus femoris (RF), biceps femoris (BF), and medial gastrocnemius (MG) were normalized to ZeroG stepping. The Lokomat required 30% of VO2 peak (2METS) compared to ~54% (3METS) for Manual Treadmill and ZeroG sessions. HR was 67% of peak during Lokomat sessions compared to ~83% for Manual Treadmill and ZeroG. Muscle activation was higher in treadmill conditions compared to the ZeroG primarily due to increased BF activity. At the same level of BWS, locomotion using the Manual Treadmill or the ZeroG is more aerobically demanding than the Lokomat. Treadmill modalities encourage greater hip extensor activation compared to overground locomotion.
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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) | 0.000 | 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) | 0.002 | 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 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".