Evaluation of a Prosthetic Swing-Phase Controller With Electrical Power Generation
Bibliographic record
Abstract
With the increased presence of microprocessor-based prostheses in the market place, the availability of a self-energizing system has practical applicability. At present, most commercially available systems require the user to routinely recharge on-board batteries, which reduces the utility of these prostheses. To address this limitation, we have proposed a unique system based on an electromechanical generator to not only continually recharge batteries that are on-board the prostheses, but to also serve as a real time swing-phase damper. A prototype system was developed and evaluated with three active individuals with above-knee amputations across four damping conditions and two gait speeds. Gait and power generation performance were assessed via selected temporal, kinematic and kinetic parameters. Gait parameters including cadence and knee angle symmetry were found to be acceptable when knee damping was adapted for each participant. Across the three subjects and two walking speeds, between 0.57 and 1.57 W of electrical power was produced. These results indicate that this technology may be utilized for prosthetic swing-phase control and ultimately may alleviate the need for manually charging of microprocessor-based prostheses.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".