Predictive Simulations of Gait with Exoskeletons that Alter Energetics
Bibliographic record
Abstract
Abstract Robotic exoskeletons, designed to augment human locomotion, have the potential to restore function in those with mobility impairments and enhance it in able-bodied individuals. However, optimally controlling these devices, to work in concert with complex and diverse human users, is a challenge. Accurate model simulations of the interaction between exoskeletons and walking humans may expedite the design process and improve control. Here, we use predictive gait simulations to investigate the effect of an exoskeleton that alters the energetic consequences of walking. To validate our approach, we re-created an past experimental paradigm where robotic exoskeletons were used to shift people’s energetically optimal step frequency to frequencies higher and lower than normally preferred. To match the experimental controller, we modelled a knee-worn exoskeleton that applied resistive torques that were either proportional or inversely proportional to step frequency—decreasing or increasing the energy optimal step frequency, respectively. We were able to replicate the experiment, finding higher and lower optimal step frequencies than in natural walking under each respective condition. Our simulated resistive torques and objective landscapes resembled the measured experimental resistive torque and energy landscapes. Individual muscle energetics revealed distinct coordination strategies consistent with each exoskeleton controller condition. Predicted step frequency and energetic outcomes were best achieved by increasing the number of virtual participants (varying whole-body anthropometrics), rather than number of muscle parameter sets (varying muscle anthropometrics). In future, our approach can be used to design controllers in advance of human testing, to help identify reasonable solution spaces or tailor design to individual users.
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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 | 0.001 |
| 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".