Lower Limb-Driven Energy Harvester: Modeling, Design, and Performance Evaluation
Bibliographic record
Abstract
Biomechanical energy harvesters (BMEHs) have shown that useable amounts of electricity can be generated from daily movement. Where access to an electrical power grid is limited, BMEHs are a viable alternative to accommodate energy requirements for portable electronics. In this paper, we present the detailed design and dynamic model of a lower limb-driven energy harvester that predicts the device output and the load on the user. Comparing with existing harvester models, the novelty of the proposed model is that it incorporates the energy required for useful electricity generation, stored inertial energy, and both mechanical and electrical losses within the device. The model is validated with the lower limb-driven energy harvester in 12 unique configurations with a combination of four different motor and three different electrical resistance combinations (3.5 Ω, 7 Ω, and 12 Ω). A case study shows that the device can generate between 3.6 and 15.5 W with an efficiency between 39.8% and 72.5%. The model was able to predict the harvester output peak voltage within 5.6 ± 3.2% error and the peak force it exerts on the user within 9.9 ± 3.4% error over a range of parameter values. The model will help to identify configurations to achieve a high harvester efficiency and provide a better understanding of how parameters affect both the timing and magnitude of the load felt by the user.
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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 | 0.001 |
| 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 | 0.001 |
| 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".