UL-DD: A Multimodal Drowsiness Dataset Using Video, Biometric Signals, and Behavioral Data

• URL: http://arxiv.org/abs/2507.13403v1
• Date: Wed, 16 Jul 2025 21:44:25 GMT
• Title: UL-DD: A Multimodal Drowsiness Dataset Using Video, Biometric Signals, and Behavioral Data
• Authors: Morteza Bodaghi, Majid Hosseini, Raju Gottumukkala, Ravi Teja Bhupatiraju, Iftikhar Ahmad, Moncef Gabbouj,
• Abstract summary: This dataset includes 3D facial video using a depth camera, IR camera footage, posterior videos, and biometric signals such as heart rate, electrodermal activity, blood oxygen saturation, skin temperature, and accelerometer data.<n>Drowsiness levels were self-reported every four minutes using the Karolinska Sleepiness Scale (KSS)<n>This study aims to create a comprehensive multimodal dataset of driver drowsiness that captures a wider range of physiological, behavioral, and driving-related signals.
• Score: 11.879350713051698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we present a comprehensive public dataset for driver drowsiness detection, integrating multimodal signals of facial, behavioral, and biometric indicators. Our dataset includes 3D facial video using a depth camera, IR camera footage, posterior videos, and biometric signals such as heart rate, electrodermal activity, blood oxygen saturation, skin temperature, and accelerometer data. This data set provides grip sensor data from the steering wheel and telemetry data from the American truck simulator game to provide more information about drivers' behavior while they are alert and drowsy. Drowsiness levels were self-reported every four minutes using the Karolinska Sleepiness Scale (KSS). The simulation environment consists of three monitor setups, and the driving condition is completely like a car. Data were collected from 19 subjects (15 M, 4 F) in two conditions: when they were fully alert and when they exhibited signs of sleepiness. Unlike other datasets, our multimodal dataset has a continuous duration of 40 minutes for each data collection session per subject, contributing to a total length of 1,400 minutes, and we recorded gradual changes in the driver state rather than discrete alert/drowsy labels. This study aims to create a comprehensive multimodal dataset of driver drowsiness that captures a wider range of physiological, behavioral, and driving-related signals. The dataset will be available upon request to the corresponding author.

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