Simulation of Energy Regeneration in Human Locomotion for Efficient Exoskeleton Actuation
Bibliographic record
Abstract
Abstract Backdriveable actuators with energy regeneration can improve the efficiency and extend the battery-powered operating times of robotic lower-limb exoskeletons by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, previous related studies have focused on steady-state level-ground walking. To better encompass real-world community mobility, here we developed a feedforward human-exoskeleton energy regeneration system model to simulate energy regeneration and storage during other daily locomotor activities. Data from inverse dynamics analyses of 10 healthy young adults walking at variable speeds and slopes were used to calculate the negative joint mechanical power and work (i.e., the mechanical energy theoretically available for electrical energy regeneration). These human joint mechanical energetics were then used to simulate backdriving a robotic exoskeleton and regenerating energy. An empirical characterization of the exoskeleton device was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration. Our performance calculations showed that regenerating energy at slower walking speeds and decline slopes could significantly extend the battery-powered operating times of robotic lower-limb exoskeletons (i.e., up to 99% increase in total number of steps), therein improving locomotor efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".