Sensors-Enabled Human State Monitoring System for Tactical Settings
Bibliographic record
Abstract
There is potential great value in turning physiological and behavioral data into actionable information in tactical environments. However, the design of an appropriate system in terms of measurement accuracy, wearing comfort and technical feasibility, for instance for forces training in realistic conditions, therefore requires addressing multiple scientific and technical challenges. The current paper focuses on work realized to address four challenges identified in recent efforts in the development of an integrated wearable system. These four challenges pertain to: 1) Data management; 2) Wearable sensors; 3) Algorithms and models; and 4) Human factors considerations. Components were developed and integrated to tackle each of the four challenges and the integrated system was tested on participants during field trials. The system referred to “Readiness Evaluation: QUantified INdividuals” (REQUIN), offers a dashboard to visualize in real time the various metrics collected and calculated by the system, including metrics derived from wearable sensors and models. Results of the field trials are discussed in regard with the four challenges addressed in the paper and recommendations for further research are presented, including using alternative sensing technologies for blood oxygenation measures and improving the models’ specificity. This study represents a critical step in the integration of real-time sensing technologies for applications involving collective situation management and control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".