Title

Application of Voice Analysis Programs to Quantify Fatigue

Presenter Information

Robert Block

Department

Biological Sciences

Major

Chemistry

Research Advisor

Thimgan, Matthew S.

Advisor's Department

Biological Sciences

Funding Source

Missouri S&T OURE Program

Abstract

Fatigue can have disastrous consequences in surgery, transportation, and monitoring tasks. It would be useful to quantitatively identify the fatigue level of an employee to predict if they are at risk for a catastrophic error. Previous efforts to quantify include subjective measures, cognitive ability and motor skills. These metrics are currently inadequate to assess sleepiness in a real-world situation. We will investigate if quantifiable speech patterns correlate with fatigue. Quantitative information on how sleepy a person is could help determine how fit they are for activities. To test this hypothesis, subjects will record a test phrase in the morning and evening 2 times a week. Features of the voice will be quantified by voice analysis software and correlated with subjective sleepiness metrics, conventional cognitive and motor skills based fatigue tests. If a reliable correlation is found, a model will then be developed to predict fatigue using speech patterns.

Biography

Robert Block is a Junior studying Chemistry with an emphasis on Pre-Medicine and a minor in Biological Science. After graduating, he plans on attending medical school and becoming an anesthesiologist. His interests include robotics, martial arts, and video games.

Presentation Type

OURE Fellows Proposal Oral Applicant

Document Type

Presentation

Award

2016-2017 OURE Fellows recipient

Location

Turner Room

Presentation Date

11 Apr 2016, 1:00 pm - 1:20 pm

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Apr 11th, 1:00 PM Apr 11th, 1:20 PM

Application of Voice Analysis Programs to Quantify Fatigue

Turner Room

Fatigue can have disastrous consequences in surgery, transportation, and monitoring tasks. It would be useful to quantitatively identify the fatigue level of an employee to predict if they are at risk for a catastrophic error. Previous efforts to quantify include subjective measures, cognitive ability and motor skills. These metrics are currently inadequate to assess sleepiness in a real-world situation. We will investigate if quantifiable speech patterns correlate with fatigue. Quantitative information on how sleepy a person is could help determine how fit they are for activities. To test this hypothesis, subjects will record a test phrase in the morning and evening 2 times a week. Features of the voice will be quantified by voice analysis software and correlated with subjective sleepiness metrics, conventional cognitive and motor skills based fatigue tests. If a reliable correlation is found, a model will then be developed to predict fatigue using speech patterns.