Using Dynamic Simulations to Estimate the Feasible Stability Region of Feet-In-Place Balance Recovery for Lower-Limb Exoskeleton Users
Bibliographic record
Abstract
In recent years, research into the balancing ca-pabilities of lower-limb exoskeletons has increased with hopes of achieving “crutch-less” stance and ambulation. However, achieving upright stability in underactuated bipedal robotics is difficult. Disturbances due to end-user interactions and actuator limitations further complicate any solutions. The current study was therefore aimed at establishing the generalized balancing capabilities of active robotic lower-limb exoskeletons through the use of predictive dynamic simulations. The ability to balance was assessed through the use of the feasible stability region (FSR), which is the region in whole-body center of mass (COM) position-velocity space where it is possible to recover upright balance through termination of the COM velocity. Direct collocation optimal control was used to estimate the baseline FSR for the human-only and human-exoskeleton system under various conditions. Additionally, Pareto optimization was used to establish trade-offs between the FSR and the motor torques that generate the necessary balance strategies, which determine the FSR. In general, our results indicated that baseline human-only and human-exoskeleton systems share similar balancing capabilities in terms of the FSR, regardless of the device's end-user mobility; however, features of the exoskeleton like high joint-level impedance and a shifted center of mass have detrimental impacts to the overall FSR size. Results from the Pareto optimization suggest that the full FSR can be nearly reached with a fraction of the required motor torques, thus protecting both the device and user. Future work will expand the current analyses to stepping strategies and control-design implementation in the Technaid Exo-H3.
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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 | 0.001 | 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".