Toward Effective Sleepiness Simulation: Validation Using Perceptual and Physiological Measures

Sleepiness results in an increased susceptibility to workplace accidents due to reduced cognitive functioning. Early detection of signs of sleepiness is crucial for mitigating work-related risks. Establishing a comprehensive database of sleepiness data is fundamental for developing effective sleepin...

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Bibliographic Details
Main Authors: Moon, Jihye, Gupta, Yashvi, Chon, Ki H.
Format: Conference Proceeding
Language:English
Subjects:
Data models
Electric potential
Electrocardiogram
Electrocardiography
Electrodermal Activity
Employment
Limiting
Particle measurements
Physiology
Signal Processing
Sleep
Sleepiness
Speech
Statistical Analysis
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Summary:Sleepiness results in an increased susceptibility to workplace accidents due to reduced cognitive functioning. Early detection of signs of sleepiness is crucial for mitigating work-related risks. Establishing a comprehensive database of sleepiness data is fundamental for developing effective sleepiness identification models. However, obtaining data on realistic sleepiness proves challenging in practice. This study explores a more efficient approach to sleepiness simulation to address this challenge. In this paper, we hypothesize that actively inducing feelings of sleepiness can precipitate sleepiness. In our experiment, ten participants simulated sleepiness while listening to a speech delivered by a tired, sleep-deprived individual. We measured self-reported levels of sleepiness and two widely-used physiological biomarkers-electrodermal activity (EDA) and electrocardiogram (ECG) signals-during this induced state. Our results shed light on the potential of this sleepiness simulation approach, unveiling physiological markers linked to induced sleepiness. By introducing this innovative sleepiness simulation method, our research advances building large sleepy datasets, which contribute to the proactive identification of sleepiness-related risks in the workplace.
ISSN:2376-8894
DOI:10.1109/BSN63547.2024.10780541