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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".