Incorporating the Qualitative Variable Comfort into the Design of a Wearable Body Sensor System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The design of mechatronic devices commonly includes qualitative design objectives that can play a significant role in the consumer appeal and the success of the product. This article presents an approach to incorporate the qualitative design objective comfort into the design process of a wearable device by determining the most suitable locations to mount such hardware. The objective is modeled through multiple criteria to represent comfort. The model is formulated through the theory of fuzzy measures and the Choquet integral. The fuzzy measures are determined through two different and new methods. One method uses a least squares algorithm and the other determines fuzzy measures using a preference ranking of alternatives. In this approach, the preference rankings are locations, ordered from the most comfortable to the least comfortable. This order of locations can be replicated with the determined fuzzy measures and Choquet integral, to build the comfort model. An error that quantifies the inaccuracy in the order of preferred to nonpreferred alternatives is introduced. The comfort model that is established in this article is validated using a training set and a test set. A comparison between the two methods that determine the fuzzy measures is presented.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it