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Updated: Feb 26, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

A multimodal drowsiness dataset using video, biometric, and behavioral data.

Morteza Bodaghi¹, Majid Hosseini¹, Raju Gottumukkala²

¹University of Louisiana at LAfayette, lafayette, LA, USA.

Scientific Data

|February 24, 2026

Summary

This summary is machine-generated.

This study introduces a new public dataset for driver drowsiness detection, combining facial, behavioral, and biometric data. The dataset captures gradual drowsiness changes over 40-minute sessions, offering a richer understanding of driver states.

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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Area of Science:

Multimodal driver monitoring
Biometric signal analysis
Human-computer interaction in vehicles

Background:

Driver drowsiness is a major safety concern, leading to accidents.
Existing datasets often lack multimodal integration or continuous state monitoring.
Accurate detection requires comprehensive physiological and behavioral data.

Purpose of the Study:

To introduce a comprehensive, publicly available multimodal dataset for driver drowsiness detection.
To capture continuous physiological and behavioral changes associated with drowsiness.
To facilitate research in advanced driver assistance systems and road safety.

Main Methods:

Collected multimodal data from 19 subjects over 1400 minutes.
Included 3D facial video, infrared, posture, heart rate, electrodermal activity, blood oxygen, skin temperature, accelerometer, grip, and telemetry data.
Utilized the Karolinska Sleepiness Scale (KSS) for continuous drowsiness self-reporting.

Main Results:

Dataset features continuous 40-minute sessions per subject, totaling 1400 minutes.
Captured gradual transitions between alert and drowsy states, not just discrete labels.
Integrated diverse signals including facial expressions, posture, and physiological metrics.

Conclusions:

The presented dataset offers a valuable resource for developing robust driver drowsiness detection systems.
Continuous monitoring and multimodal integration provide a more realistic representation of driver states.
This dataset will advance research in real-time driver safety and autonomous driving technologies.