Harnessing Biomechanical Energy Using Shoulder Exoskeleton: Electrical Energy Regeneration
Bibliographic record
Abstract
Abstract Implementing back-drivable actuators with energy regeneration capabilities can improve the efficiency and extend the battery life of robotic exoskeletons, by converting a portion of dissipated energy during negative mechanical work into electrical energy. We present the first study of the regeneration capabilities of shoulder exoskeletons, which includes experimental motor parameter identification, the development of an electromechanical upper-limb human-exoskeleton model for energy regeneration, and task-based experiments assessing real-world energy generation despite inherent motor limitations. Initial steps involve motor parameter identification using a joint dynamometer system and modeling of the human-exoskeleton system with a specific emphasis on energy regeneration in the shoulder region. Task-based experiments were conducted to evaluate energy regeneration during daily exoskeleton usage. Our experimental identification reveals that the regenerated current follows a third-degree polynomial relationship with joint angular speed. Task-based simulations indicate that electrical energy recovery ranges from 30.4μJ during slow motions to 1.02 mJ during very fast movements. Efficiency analysis shows that performance peaks at approximately 60deg/s, where the trade-off between increased current generation and rising frictional losses is optimized. Despite challenges such as limited electricity generation due to motor characteristics, our findings demonstrate the feasibility of energy regeneration in shoulder exoskeletons, albeit with certain constraints.
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".