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The Use of Physiological Data and Machine Learning to Detect Stress Events for Adaptive Automation.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
The Use of Physiological Data and Machine Learning to Detect Stress Events for Adaptive Automation./
作者:
Falkenberg, Zachary.
面頁冊數:
1 online resource (57 pages)
附註:
Source: Masters Abstracts International, Volume: 85-02.
Contained By:
Masters Abstracts International85-02.
標題:
Computer engineering. -
電子資源:
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:
569006
Computer 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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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.
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