Thermal Modeling and Characterization of Twisted Coiled Actuators for Upper Limb Wearable Devices
Bibliographic record
Abstract
A key issue with wearable technology today is the popular use of stiff electrical motors to actuate the joints. Although their performance is ideal in terms of power, bandwidth, accuracy, and speed, they do not have the inherent ability to replicate many key biological properties. This includes the ability to modulate compliance, produce linear actuation, and provide high power-to-weight ratios. The solutions to replicating these properties usually include complex mechanisms in the form of gear boxes, parallel spring mechanisms, and control algorithms, making these devices costly, heavy, and large. As an alternative, the twisted coiled actuator (TCA) is flexible, soft, and can provide large strain and high power outputs when thermally activated. This article provides a new solution to modeling the thermal behavior of TCAs within an active air cooled enclosure. The thermal models were validated against multiple inner tube diameters, heating powers, cooling pressures, and TCA sizes, and predicted the temperature to within 8.16°C during heating, and 4.33°C during cooling, corresponding to 92.7% and 94.5% accuracy, respectively. After model validation, the sensitivity of the input design parameters to performance characteristics, such as system bandwidth and efficiency, were investigated to provide specific use cases on model optimization techniques.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".