Developing safe fall strategies for lower limb exoskeletons
Bibliographic record
Abstract
One of the main challenges in the use of a powered lower limb exoskeleton (LLE) is to ensure that balance is maintained throughout the operation of the device. Since no control strategy has yet been implemented that prevents falls in the case of a loss of balance, head or other serious injuries may occur during independent use of LLEs in the event of a fall. These safety concerns limit LLEs in the community to supervised use only. Using the backward fall as a model, we used optimization techniques to develop safe fall control strategies in order to avoid head impact and mitigate the impact velocity of the hips. From available human biomechanics data, we first developed an optimization methodology to study falls of healthy people. The results showed similar kinematic and dynamic characteristics to findings of previous studies on real-life human falls. Second, we extended the optimization methodology to include characteristics of a hypothetical LLE and to generate optimal joint trajectories and optimal torque profiles for the fall duration. The results revealed that by applying the optimal fall strategy, the severity of a simulated fall was minimized compared to when the device fell with locked joints (i.e., how currently used exoskeletons fall): head impact was avoided and hip impact velocity was reduced by more than 50%.
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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".