Clinical trials of GPE-based muscle support algorithm for robotic hip exoskeleton: a pilot study
Bibliographic record
Abstract
With the advent of an aging society, the lack of physical activity has become a major concern, leading to various age-related diseases. To prevent such issues, research on wearable robots aimed at improving gait has been actively pursued. Among them, exoskeleton robots, a widely used approach, require an accurate understanding of the user's gait cycle for effective control. Various studies have explored gait cycle detection and prediction methods depending on the type of gait robot platform and the use of sensors. However, a major challenge in gait cycle prediction algorithms remains the issue of nonlinear predictive trajectories. In the study, a robotic hip exoskeleton (RHE) was utilized to implement an enhanced gait phase estimation (GPE) algorithm integrated with a muscle support system. Participants were divided into two groups (Group A and Group B) based on their initial gait performance, and the effectiveness of gait rehabilitation training was evaluated. The results showed that in the 10-meter walk test (10MWT), walking time decreased by approximately 5% in Group A and 27% in Group B. In the 6-minute walk test (6MinWT), walking distance increased by approximately 1% in Group A and 14% in Group B. Group B, which had lower initial gait performance, showed a greater gait performance improvement rate compared to Group A, which had higher initial gait performance. Through the gait performance results of the two groups, the applicability of the GPE based muscle support algorithm was confirmed.
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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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".