A Comprehensive Review of Unobtrusive Biosensing in Intelligent Vehicles: Sensors, Algorithms, and Integration Challenges
Bibliographic record
Abstract
Unobtrusive in-vehicle measurement and the monitoring of physiological signals have recently attracted researchers in industry and academia as an innovative approach that can provide valuable information about drivers' health and status. The main goal is to reduce the number of traffic accidents caused by driver errors by monitoring various physiological parameters and devising appropriate actions to alert the driver or to take control of the vehicle. The research on this topic is in its early stages. While there have been several publications on this topic and industrial prototypes made by car manufacturers, a comprehensive and critical review of the current trends and future directions is missing. This review examines the current research and findings in in-vehicle physiological monitoring and suggests future directions and potential uses. Various physiological sensors, their potential locations, and the results they produce are demonstrated. The main challenges of in-vehicle biosensing, including unobtrusive sensing, vehicle vibration and driver movement cancellation, and privacy management, are discussed, and possible solutions are presented. The paper also reviews the current in-vehicle biosensing prototypes built by car manufacturers and other researchers. The reviewed methods and presented directions provide valuable insights into robust and accurate biosensing within vehicles for researchers in the field.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".