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The Use of Physiological Data and Machine Learning to Detect Stress Events for Adaptive Automation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	The Use of Physiological Data and Machine Learning to Detect Stress Events for Adaptive Automation./
Author:	Falkenberg, Zachary.
Description:	1 online resource (57 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-02.
Contained By:	Masters Abstracts International85-02.
Subject:	Engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380098717

The Use of Physiological Data and Machine Learning to Detect Stress Events for Adaptive Automation.
Falkenberg, Zachary.

The Use of Physiological Data and Machine Learning to Detect Stress Events for Adaptive Automation. - 1 online resource (57 pages)

Source: Masters Abstracts International, Volume: 85-02.

Thesis (M.S.E.)--The University of Akron, 2023.

Includes bibliographical references

Human factors concerns with automation have emerged as contributing factors in many aviation accidents in the past few decades. Adaptive automation, where a system dynamically assigns tasks to automation or the pilot based on workload, has been proposed as a potential solution to many of these concerns. This study examines how one proposed method of adaptive automation, using physiological data to measure workload, could be implemented using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), and facial electromyography (fEMG) data was collected at both low and high workload while subjects completed common tasks performed by pilots. This data was used to train binary classification neural networks, with many models achieving high accuracy. The models were then applied to different data with varying workload, achieving poor results. The results of this study identify design requirements for adaptive automation systems using this method, and further study required for practical application.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380098717Subjects--Topical Terms:

561152
Engineering.
Subjects--Index Terms:

Adaptive automationIndex Terms--Genre/Form:

554714
Electronic books.

The Use of Physiological Data and Machine Learning to Detect Stress Events for Adaptive Automation.
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