SWINGING LEG CONTROL OF A LOWER LIMB EXOSKELETON VIA A SHOE WITH IN-SOLE SENSING
Bibliographic record
Abstract
A lower limb exoskeleton can help in weight-bearing and walking to assist laborers doing heavy work. For exoskeleton-assisted walking, the wearing comfort and walking convenience are important so there must be minimal interference with leg movement. Hence, a peculiar design strategy based on an in-sole sensing shoe is presented to achieve real-time motion detection and follow-up control of the moving leg. Compared to the elastic muscle extension, the sensor must exhibit minimal deflection under load. Therefore, an ultra-thin structure integrating 6 bar linkages and 3 cantilevers has been used in the design of the in-sole sensing shoe which can detect force in two directions and torque in one. A swing phase experiment and a random leg motion test were carried out. Results show validity of human motion detection and follow-up control strategy based on this plantar surface sensor.
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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".