Intelligent control of a smart walker and its performance evaluation
Bibliographic record
Abstract
Recent technological advances have allowed the development of force-dependent, intelligently controlled smart walkers that are able to provide users with enhanced mobility, support and gait assistance. The purpose of this study was to develop an intelligent rule-based controller for a smart walker to achieve a smooth interaction between the user and the walker. This study developed a rule-based mapping between the interaction force, measured by a load cell attached to the walker handle, and the acceleration of the walker. Ten young, healthy subjects were used to evaluate the performance of the proposed controller compared to a well-known admittance-based control system. There were no significant differences between the two control systems concerning their user experience, velocity profiles or average cost of transportation. However, the admittance-based control system required a 1.2N lower average interaction force to maintain the 1m/s target speed (p = 0.002). Metabolic data also indicated that smart walker-assisted gait could considerably reduce the metabolic demand of walking with a four-legged walker.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".