962 research outputs found

    A comprehensive analysis of machine learning and deep learning models for identifying pilots’ mental states from imbalanced physiological data

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    This study focuses on identifying pilots' mental states linked to attention-related human performance-limiting states (AHPLS) using a publicly released, imbalanced physiological dataset. The research integrates electroencephalography (EEG) with non-brain signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and respiration, to create a deep learning architecture that combines one-dimensional Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) models. Addressing the data imbalance challenge, the study employs resampling techniques, specifically downsampling with cosine similarity and oversampling using Synthetic Minority Over-sampling Technique (SMOTE), to produce balanced datasets for enhanced model performance. An extensive evaluation of various machine learning and deep learning models, including XGBoost, AdaBoost, Random Forest (RF), Feed-Forward Neural Network (FFNN), standalone 1D-CNN, and standalone LSTM, is conducted to determine their efficacy in detecting pilots' mental states. The results contribute to the development of efficient mental state detection systems, highlighting the XGBoost algorithm and the proposed 1D-CNN+LSTM model as the most promising solutions for improving safety and performance in aviation and other industries where monitoring mental states is essential

    Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

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    This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

    Breaking Down the Barriers To Operator Workload Estimation: Advancing Algorithmic Handling of Temporal Non-Stationarity and Cross-Participant Differences for EEG Analysis Using Deep Learning

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    This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases predictive accuracy and decreases cross-participant variance. Deep learning regression approaches are applied to a multi-task environment with workload transitions. Accounting for temporal dependence significantly reduces error and increases correlation compared to baselines. Visualization techniques for LSTM feature saliency are developed to understand EEG analysis model biases

    飛行ロボットにおける人間・ロボットインタラクションの実現に向けて : ユーザー同伴モデルとセンシングインターフェース

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    学位の種別: 課程博士審査委員会委員 : (主査)東京大学准教授 矢入 健久, 東京大学教授 堀 浩一, 東京大学教授 岩崎 晃, 東京大学教授 土屋 武司, 東京理科大学教授 溝口 博University of Tokyo(東京大学

    Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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    Irish Machine Vision and Image Processing Conference Proceedings 2017

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    QUEST Hierarchy for Hyperspectral Face Recognition

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    Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided

    Human Motion Analysis for Efficient Action Recognition

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    Automatic understanding of human actions is at the core of several application domains, such as content-based indexing, human-computer interaction, surveillance, and sports video analysis. The recent advances in digital platforms and the exponential growth of video and image data have brought an urgent quest for intelligent frameworks to automatically analyze human motion and predict their corresponding action based on visual data and sensor signals. This thesis presents a collection of methods that targets human action recognition using different action modalities. The first method uses the appearance modality and classifies human actions based on heterogeneous global- and local-based features of scene and humanbody appearances. The second method harnesses 2D and 3D articulated human poses and analyizes the body motion using a discriminative combination of the parts’ velocities, locations, and correlations histograms for action recognition. The third method presents an optimal scheme for combining the probabilistic predictions from different action modalities by solving a constrained quadratic optimization problem. In addition to the action classification task, we present a study that compares the utility of different pose variants in motion analysis for human action recognition. In particular, we compare the recognition performance when 2D and 3D poses are used. Finally, we demonstrate the efficiency of our pose-based method for action recognition in spotting and segmenting motion gestures in real time from a continuous stream of an input video for the recognition of the Italian sign gesture language

    Physiological Approach To Characterize Drowsiness In Simulated Flight Operations During Window Of Circadian Low

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    Drowsiness is a psycho-physiological transition from awake towards falling sleep and its detection is crucial in aviation industries. It is a common cause for pilot’s error due to unpredictable work hours, longer flight periods, circadian disruption, and insufficient sleep. The pilots’ are prone towards higher level of drowsiness during window of circadian low (2:00 am- 6:00 am). Airplanes require complex operations and lack of alertness increases accidents. Aviation accidents are much disastrous and early drowsiness detection helps to reduce such accidents. This thesis studied physiological signals during drowsiness from 18 commercially-rated pilots in flight simulator. The major aim of the study was to observe the feasibility of physiological signals to predict drowsiness. In chapter 3, the spectral behavior of electroencephalogram (EEG) was studied via power spectral density and coherence. The delta power reduced and alpha power increased significantly (
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