# A Survey on Drowsiness Detection -- Modern Applications and Methods

arXiv: https://arxiv.org/abs/2408.12990 · HTML: https://mlsanity.com/paper/2408.12990/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 190 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models](https://mlsanity.com/paper/2308.04995/) (2023)
- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [Synthetic Data for Face Recognition: Current State and Future Prospects](https://mlsanity.com/paper/2305.01021/) (2023)
- [PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation](https://mlsanity.com/paper/2108.10710/) (2021)
- [Multi-channel EEG recordings during a sustained-attention driving task](https://mlsanity.com/paper/1809.06534/) (2018)
- [Self-restrained Triplet Loss for Accurate Masked Face Recognition](https://mlsanity.com/paper/2103.01716/) (2021)
- [EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network](https://mlsanity.com/paper/2107.09507/) (2021)
- [A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection](https://mlsanity.com/paper/1904.07312/) (2019)
- [QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization](https://mlsanity.com/paper/2206.10526/) (2022)
- [Bias Remediation in Driver Drowsiness Detection systems using Generative Adversarial Networks](https://mlsanity.com/paper/1912.12123/) (2019)
- [A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks](https://mlsanity.com/paper/2206.06544/) (2022)
- [Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques](https://mlsanity.com/paper/1811.01627/) (2018)
- [Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation](https://mlsanity.com/paper/2109.08355/) (2021)
- [Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework](https://mlsanity.com/paper/2209.01401/) (2022)
- [Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model](https://mlsanity.com/paper/2112.10894/) (2021)
- [EEG-Based Driver Drowsiness Estimation Using Convolutional Neural Networks](https://mlsanity.com/paper/1809.00929/) (2018)
- [Drowsiness Detection for Office-based Workload with Mouse and Keyboard Data](https://mlsanity.com/paper/1909.04580/) (2019)
- [EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning](https://mlsanity.com/paper/2001.02399/) (2020)
- [Towards Real-time Drowsiness Detection for Elderly Care](https://mlsanity.com/paper/2010.10771/) (2020)
- [Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis](https://mlsanity.com/paper/2204.04533/) (2022)

## Built on by (most cited first, 4 of 4)

- [LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection](https://mlsanity.com/paper/2507.21756/) (2025)
- [Privacy-Preserving Driver Drowsiness Detection with Spatial Self-Attention and Federated Learning](https://mlsanity.com/paper/2508.00287/) (2025)
- [Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2601.07556/) (2026)
- [Enhancing Road Safety: An IoT-Based Accident Detection and Prevention Mechanism](https://mlsanity.com/paper/2606.22381/) (2026)

## Abstract (arXiv)

Drowsiness detection holds paramount importance in ensuring safety in workplaces or behind the wheel, enhancing productivity, and healthcare across diverse domains. Therefore accurate and real-time drowsiness detection plays a critical role in preventing accidents, enhancing safety, and ultimately saving lives across various sectors and scenarios. This comprehensive review explores the significance of drowsiness detection in various areas of application, transcending the conventional focus solely on driver drowsiness detection. We delve into the current methodologies, challenges, and technological advancements in drowsiness detection schemes, considering diverse contexts such as public transportation, healthcare, workplace safety, and beyond. By examining the multifaceted implications of drowsiness, this work contributes to a holistic understanding of its impact and the crucial role of accurate and real-time detection techniques in enhancing safety and performance. We identified weaknesses in current algorithms and limitations in existing research such as accurate and real-time detection, stable data transmission, and building bias-free systems. Our survey frames existing works and leads to practical recommendations like mitigating the bias issue by using synthetic data, overcoming the hardware limitations with model compression, and leveraging fusion to boost model performance. This is a pioneering work to survey the topic of drowsiness detection in such an entirely and not only focusing on one single aspect. We consider the topic of drowsiness detection as a dynamic and evolving field, presenting numerous opportunities for further exploration.