An Approach to Optimize Multiple Design Objectives With Qualitative and Quantitative Criteria for a Wearable Body Sensor System
Bibliographic record
Abstract
This paper presents an approach to optimize multiple design objectives that have qualitative and quantitative design variables, with specific application for a wearable body sensor system. The methodology incorporates a way to group the qualitative and quantitative design variables and the design objectives that are present in the problem and the establishment of the domains that exist in such a design problem. Design indices are the objective functions that represent the qualitative design goals. The design space is systematically reduced thereby making it easier to decide on an optimal design solution, specifically to pick a good solution from the non-dominated solutions. A technique of fuzzy logic is used to assign weights to the subjective and non-crisp design criteria. With the method presented in this paper it is illustrated how to design a wearable body sensor system with respect to comfort and reliability. Specifically, the design of an electroencephalography/electrooculography monitoring system for sleep monitoring is considered, where the presented approach provides a systematic way to find an appropriate number and type of components, their locations, and how they are connected and structured.
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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 | 0.002 | 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 | 0.001 | 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".