ArticleSensors (Basel, Switzerland)2023
A CNN-Based Wearable System for Driver Drowsiness Detection.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 35 citations in OpenAlex.
- Domain-robust vision transformer with hierarchical swin encoding for explainable low-latency driver drowsiness detection.Scientific reports · 2026Article
- GACNet: A graph attention-based neural network for electroencephalography localization via contrastive learning.Scientific reports · 2026Article
- Digital Technology for Sleep Symptoms in Parkinson's Disease: A Scoping Review.Journal of movement disorders · 2026Article
- Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning.Scientific reports · 2025Article
- Real-Time Pupil Localization Algorithm for Blurred Images Based on Double Constraints.Sensors (Basel, Switzerland) · 2025Article
- Wireless ear EEG to monitor drowsiness.Nature communications · 2024Article
- Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.Sensors (Basel, Switzerland) · 2023Article
- A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Drowsiness poses a serious challenge to road safety and various in-cabin sensing technologies have been experimented with to monitor driver alertness. Cameras offer a convenient means for contactless sensing, but they may violate user privacy and require complex algorithms to accommodate user (e.g., sunglasses) and environmental (e.g., lighting conditions) constraints. This paper presents a lightweight convolution neural network that measures eye closure based on eye images captured by a wearable glass prototype, which features a hot mirror-based design that allows the camera to be installed on the glass temples. The experimental results showed that the wearable glass prototype, with the neural network in its core, was highly effective in detecting eye blinks. The blink rate derived from the glass output was highly consistent with an industrial gold standard EyeLink eye-tracker. As eye blink characteristics are sensitive measures of driver drowsiness, the glass prototype and the lightweight neural network presented in this paper would provide a computationally efficient yet viable solution for real-world applications.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.