Automated Vision-Based Detection of Impairment Through Divided Attention Psychophysical Tests
Bibliographic record
Abstract
Divided attention psychophysical tests are one of the main tests from Standardized Field Sobriety Tests (SFSTs) that Drug Recognition Expert (DRE) officers employ to detect impaired drivers and to investigate the type of consumed drugs. Two well-known divided attention psychophysical tests are One Leg Stand (OLS) and Walk and Turn (WAT), which are commonly used by officers to make a decision on the status of the drivers. As this decision might be considered by courts for further investigation, the purpose of this study is to design an automated impairment system to remove the subjectivity of SFSTs by helping officers make accurate determinations of sobriety and to serve as evidence for proving the correctness of the officers' decisions in the courts. In this study, a vision-based system is introduced and implemented to automatically detect impaired subjects using various feature engineering and machine learning algorithms, which were performed on the OLS and WAT videos obtained from 34 volunteer participants. Based on the results, the Random Forest classifier showed the best performance for impairment classification, achieving results comparable to those of DRE officers. Furthermore, the OLS-right features are the most relevant compared to the WAT features for the final classification.
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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 | 0.001 | 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) | 0.002 | 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 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".