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 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 | 0.000 | 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) | 0.000 | 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".