Development of a Novel Fully Passive Treadmill Training Paradigm for Lower Limb Therapeutic Intervention
Bibliographic record
Abstract
A simulation based study of a completely new form of body-weight supported treadmill training (BWSTT) technique which is fully passive in nature is presented in this paper. The approach does not require any powered means at the lower limbs and is implemented using a combination of coordinated joint locking/unlocking and flexible torque transfer mechanisms. The hip extension pertaining to the stance phase of the gait cycle is achieved through the stance foot being literally dragged by the treadmill belt while the required manoeuvring of the trunk is expected to be accomplished by the voluntary arm-support from the subject. The swing phase, on the other hand, is initiated through appropriately coupling the swing knee with the contralateral extending hip and eventually achieve full knee extension through switching the treadmill speed to a lower value. Considering adequate support from the able arms, the process effectively turns the frictional force at the foot-treadmill belt interface into an agent causing the required whole body mechanical energy fluctuation during the gait cycle. The simulation platform consists of a dynamic planer (sagittal) full body humanoid model along with the treadmill model developed within a CAD based software environment interfaced with passive viscoelastic joint properties implemented in Simulink. The voluntary upper body effort as well as control of the gait cycle are also developed within MATLAB/Simulink environment. The gait cycle generated using the new concept is thoroughly investigated through this simulation study.
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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".