# Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/driver-drowsiness-detection-using-behavioral-measures-and-machine-learning/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Mkhuseli Ngxande,Jules‐Raymond Tapamo,Michael Burke |
| Citations | 158 |
| DOI | 10.1109/robomech.2017.8261140 |
| Fields | Engineering,Psychology |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2783618220 |
| Type | conference-paper |
| Year | 2017 |

## Paper authors

- [Michael Burke](https://scholariq.org/researchers/michael-burke/)

## Paper primary topic

- [Sleep and Work-Related Fatigue](https://scholariq.org/topics/sleep-and-work-related-fatigue/)

## Paper topics

- [Sleep and Work-Related Fatigue](https://scholariq.org/topics/sleep-and-work-related-fatigue/)
- [Elevator Systems and Control](https://scholariq.org/topics/elevator-systems-and-control/)
- [IoT and GPS-based Vehicle Safety Systems](https://scholariq.org/topics/iot-and-gps-based-vehicle-safety-systems/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.