Biometrically Measured Affect for Screen-Based Drone Pilot Skill Acquisition

Dalilian, Fatemeh; Nembhard, David

doi:10.1080/10447318.2023.2208991

Cited by 5 publications

(1 citation statement)

References 114 publications

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“…At the same time, among the multifaceted avenues within this realm, the utilization of electroencephalogram (EEG) signals for emotion detection emerges as a captivating prospect [7]. This technology unfurls a panorama of applications that span from precisiondriven control mechanisms in activities such as drone piloting and driving to the immersive landscapes of electronic gaming [8]. However, despite its immense potential, the domain of EEG-based emotion detection has encountered limited exploration.…”

Section: Introductionmentioning

confidence: 99%

Towards Effective Emotion Detection: A Comprehensive Machine Learning Approach on EEG Signals

Ul Hassan,

Ali,

Abideen

et al. 2023

BioMedInformatics

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Emotion detection assumes a pivotal role in the evaluation of adverse psychological attributes, such as stress, anxiety, and depression. This study undertakes an exploration into the prospective capacities of machine learning to prognosticate individual emotional states, with an innovative integration of electroencephalogram (EEG) signals as a novel informational foundation. By conducting a comprehensive comparative analysis of an array of machine learning methodologies upon the Kaggle Emotion Detection dataset, the research meticulously fine-tunes classifier parameters across various models, including, but not limited, to random forest, decision trees, logistic regression, support vector machines, nearest centroid, and naive Bayes classifiers. Post hyperparameter optimization, the logistic regression algorithm attains a peak accuracy rate of 97%, a proximate performance mirrored by the random forest model. Through an extensive regimen of EEG-based experimentation, the study underscores the profound potential of machine learning paradigms to significantly elevate the precision of emotion detection, thereby catalyzing advancements within the discipline. An ancillary implication resides in early discernment capabilities, rendering this investigation pertinent within the domain of mental health assessments.

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Section: Introductionmentioning

confidence: 99%

Towards Effective Emotion Detection: A Comprehensive Machine Learning Approach on EEG Signals

Ul Hassan,

Ali,

Abideen

et al. 2023

BioMedInformatics

View full text Add to dashboard Cite

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Human Factors and AI in UAV Systems: Enhancing Operational Efficiency Through AHP and Real-Time Physiological Monitoring

Alharasees,

Kale

2024

J Intell Robot Syst

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Integrating Artificial Intelligence (AI) into Unmanned Aerial Vehicle (UAV) operations has advanced efficiency, safety, and decision-making. This study addresses critical gaps in UAV methods, including insufficient integration of human factors, operator variability, and the lack of systematic error analysis. To overcome these challenges, a novel approach combines the Analytic Hierarchy Process (AHP) with three core human factors models: the Observe-Orient-Decide-Act (OODA) loop, the Human Factors Analysis and Classification System (HFACS), and the SHELL model. An online survey was conducted across diverse UAV operator groups to prioritize critical factors within each model. Additionally, real-time monitoring of heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) was conducted during UAV operations at various automation levels with different experience levels. Visualization through boxplots and percentage change matrices provided insights into operator stress and workload across automation levels. Integrating AHP findings and physiological data revealed significant differences in operator prioritization, highlighting the need for tailored AI-UAV strategies. This research combines survey data with real-time physiological monitoring, offering visions into optimizing human-AI interaction in UAV operations and providing a foundation for improving AI integration and operator strategies.

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Cognitive and behavioral markers for human detection error in AI-assisted bridge inspection

Dalilian,

Nembhard

2024

Applied Ergonomics

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Biometrically Measured Affect for Screen-Based Drone Pilot Skill Acquisition

Cited by 5 publications

References 114 publications

Towards Effective Emotion Detection: A Comprehensive Machine Learning Approach on EEG Signals

Towards Effective Emotion Detection: A Comprehensive Machine Learning Approach on EEG Signals

Human Factors and AI in UAV Systems: Enhancing Operational Efficiency Through AHP and Real-Time Physiological Monitoring

Cognitive and behavioral markers for human detection error in AI-assisted bridge inspection

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