| 1. |
- Barua, Shaibal, et al.
(författare)
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Automated EEG Artifact Handling with Application in Driver Monitoring
- 2018
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 22:5, s. 1350-1361
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Tidskriftsartikel (refereegranskat)abstract
- Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a pre-processing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.
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| 2. |
- Barua, Shaibal, 1982-
(författare)
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Multivariate Data Analytics to Identify Driver’s Sleepiness, Cognitive load, and Stress
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Driving a vehicle in a dynamic traffic environment requires continuous adaptation of a complex manifold of physiological and cognitive activities. Impaired driving due to, for example, sleepiness, inattention, cognitive load or stress, affects one’s ability to adapt, predict and react to upcoming traffic events. In fact, human error has been found to be a contributing factor in more than 90% of traffic crashes. Unfortunately, there is no robust, objective ground truth for determining a driver’s state, and researchers often revert to using subjective self-rating scales when assessing level of sleepiness, cognitive load or stress. Thus, the development of better tools to understand, measure and monitor human behaviour across diverse scenarios and states is crucial. The main objective of this thesis is to develop objective measures of sleepiness, cognitive load and stress, which can later be used as research tools, either to benchmark unobtrusive sensor solutions or when investigating the influence of other factors on sleepiness, cognitive load, and stress.This thesis employs multivariate data analysis using machine learning to detect and classify different driver states based on physiological data. The reason for using rather intrusive sensor data, such as electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), skin conductance, finger temperature, and respiration is that these methods can be used to analyse how the brain and body respond to internal and external changes, including those that do not generate overt behaviour. Moreover, the use of physiological data is expected to grow in importance when investigating human behaviour in partially automated vehicles, where active driving is replaced by passive supervision.Physiological data, especially the EEG is sensitive to motion artifacts and noise, and when recorded in naturalistic environments such as driving, artifacts are unavoidable. An automatic EEG artifact handling method ARTE (Automated aRTifacts handling in EEG) was therefore developed. When used as a pre-processing step in the classification of driver sleepiness, ARTE increased classification performance by 5%. ARTE is data-driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered. In addition, several machine-learning algorithms have been developed for sleepiness, cognitive load, and stress classification. Regarding sleepiness classification, the best achieved accuracy was achieved using a Support Vector Machine (SVM) classifier. For multiclass, the obtained accuracy was 79% and for binary class it was 93%. A subject-dependent classification exhibited a 10% improvement in performance compared to the subject-independent classification, suggesting that much can be gained by using personalized classifiers. Moreover, by embedding contextual information, classification performance improves by approximately 5%. In regard to cognitive load classification, a 72% accuracy rate was achieved using a random forest classifier. Combining features from several data sources may improve performance, and indeed, we observed classification performance improvement by 10%-20% compared to using features from a single data source. To classify drivers’ stress, using the Case-based reasoning (CBR) and data fusion approach, the system achieved an 83.33% classification accuracy rate.This thesis work encourages the use of multivariate data for detecting and classifying driver states, including sleepiness, cognitive load, and stress. A univariate data source often presents challenges, since features from a single source or one just aspect of the feature are not entirely reliable; Therefore, multivariate information requires accurate driver state detection. Often, driver states are a subjective experience, in which other contextual data plays a vital role. Thus, the implication of incorporating contextual information in the classification scheme is presented in this thesis work. Although there are several commonalities, physiological signals are modulated differently in different driver states; Hence, multivariate data could help detect multiple driver states simultaneously – for example, cognitive load detection when a person is under the influence of different levels of stress.
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| 3. |
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| 4. |
- Leon, Miguel
(författare)
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IMPROVING DIFFERENTIAL EVOLUTION WITH ADAPTIVE AND LOCAL SEARCH METHODS
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Differential Evolution (DE) is a population-based algorithm that belongs to the Evolutionary algorithm family. During recent years, DE has become a popular algorithm in optimization due to its strength solving different types of optimization problems and due to its easy usage and implementation.However, how to choose proper mutation strategy and control parameters for DE presents a major difficulty in many real applications. Since both mutation strategy and DE control parameters are highly problem dependent, they have to be adapted to suite different search spaces and different problems. Failure in proper assignment for them will cause slow convergence in search or stagnation with a local optimum.Many researches have been conducted to tackle the above issues. The major efforts have been made in the following three directions. First, some works have been proposed that adapt the selection between various mutation strategies. But the choice of strategies in these methods has not considered the difference of quality of individuals in the population, which means that all individuals will acquire the same probability to select a mutation strategy from the candidates. This does not seem a very desired practice since solutions of large difference would require different mutation operators to reach improvement. Second, many works have been focusing on the adaptation of the control parameters of DE (mutation factor (F) and crossover rate (CR)). They mainly rely on previous successful F and CR values to update the probability functions that are used to generate new F and CR values. By doing this, they ignore the stochastic nature of the operators in DE such that weak F and CR values can also get success in producing better trial solutions. The use of such imprecise experiences of success would prevent the DE parameters from being adapted towards the most effective values in coming generations. Third, various local search methods have been incorporated into DE to enhance exploitation in promising regions so as to speed up the convergence to optima. It is important to properly adjust the characteristics of the local search in DE to achieve well balanced exploratory/exploitative behavior to solve complex optimization problems.This thesis aims to further improve the performance of DE by new adaptation and local search methods. The main results can be summarized in the following three aspects:1) Proposal of a new rank-based mutation adaptation method, which takes into account the quality of solutions in the population when adapting the selection probabilities of mutation strategies. This makes possible to treat solutions with distinct ranks (in quality) differently by using different selection probabilities for mutation operators.2) Development of improved parameter adaptation methods for DE, which emphasizes more reliable and fair evaluation of candidates (F and CR assignments) during the search process. It is suggested that greedy search being used as a fast and cheap technique to look for better parameter assignment for F and CR respectively in the neighborhood of a current candidate. Further, a joint parameter adaptation method is proposed that enables continuous update of the selection probabilities for F and CR pairs based on feedback acquired during the search.3) Proposal of new methods for better incorporation of local search into a DE algorithm. The Eager Random Search method is investigated as local search inside DE, which exhibits different exploratory-exploitative characteristics by using different probability density functions. More importantly, we propose a novel memetic framework in which Alopex local search (ALS) is performed in collaboration with a DE algorithm. The framework favors seamless connection between exploration and exploitation in the sense that the behavior of exploitation by ALS can be controlled by the status of global exploration by DE.The proposed methods and algorithms have been tested in a number of benchmark problems, obtaining competitive results compared with the state-of-the-art algorithms. Additionally, the Greedy Adaptive DE (GADE) algorithm (developed based on greedy search for DE parameters) has been tested in a real industrial problem, i.e., finding best component parameters to optimize the performance of harmonic filters for power transmission. GADE is shown to produce better harmonic filter systems with lower harmonic distortion than the standard DE.
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| 5. |
- Rahman, Hamidur, 1984-
(författare)
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An Intelligent Non-Contact based Approach for Monitoring Driver’s Cognitive Load
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The modern cars have been equipped with advanced technical features to help make driving faster, safer and comfortable. However, to enhance transport security i.e. to avoid unexpected traffic accidents it is necessary to consider a vehicle driver as a part of the environment and need to monitor driver’s health and mental state. Driving behavior-based and physiological parameters-based approaches are the two commonly used approaches to monitor driver’s health and mental state. Previously, physiological parameters-based approaches using sensors are often attached to the human body. Although these sensors attached with body provide excellent signals in lab conditions it can often be troublesome and inconvenient in driving situations. So, physiological parameters extraction based on video images offers a new paradigm for driver’s health and mental state monitoring. This thesis report presents an intelligent non-contact-based approach to monitor driver’s cognitive load based on physiological parameters and vehicular parameters. Here, camera sensor has been used as a non-contact and pervasive methods for measuring physiological parameters.The contribution of this thesis is in three folds: 1) Implementation of a camera-based method to extract physiological parameters e.g., heart rate (HR), heart rate variability (HRV), inter-bit-interval (IBI), oxygen saturation (SpO2) and respiration rate (RR) considering several challenging conditions e.g. illumination, motion, vibration and movement. 2) Vehicular parameters e.g. lateral speed, steering wheel angle, steering wheel reversal rate, steering wheel torque, yaw rate, lanex, and lateral position extraction from a driving simulator. 3) Investigation of three machine learning algorithms i.e. Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) to classify driver’s cognitive load. Here, according to the results, considering the challenging conditions, the highest correlation coefficient achieved for both HR and SpO2 is 0.96. Again, the Bland Altman plots shows 95% agreement between camera and the reference sensor. For IBI, the quality index (QI) is achieved 97.5% considering 100 ms R-peak error. For cognitive load classification, two separate studies are conducted, study1 with 1-back task and study2 with 2-back task and both time domain and frequency domain features are extracted from the facial videos. Finally, the achieved average accuracy for the classification of cognitive load is 91% for study1 and 83% for study2. In future, the proposed approach should be evaluated in real-road driving environment considering other complex challenging situations such as high temperature, complete dark/bright environment, unusual movements, facial occlusion by hands, sunglasses, scarf, beard etc.
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| 6. |
- Tomasic, Ivan, et al.
(författare)
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Mixed-Effect Models for the Analysis and Optimization of Sheet-Metal Assembly Processes
- 2017
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Ingår i: IEEE Transactions on Industrial Informatics. - 1551-3203 .- 1941-0050. ; 13:5, s. 2194-2202
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Tidskriftsartikel (refereegranskat)abstract
- Assembly processes can be affected by various parameters, which is revealed by the measured geometrical characteristics (GCs) of the assembled parts deviating from the nominal values. Here, we propose a mixed-effect model (MEM) application for the purposes of analyzing variations in assembly cells, as well as for screening the input variables and characterization. MEMs make it possible to take into account statistical dependencies that originate from repeated measurements on the same assembly. The desirability functions approach was used to describe how to find corrective or control actions based on the fitted MEM. Objectives: To examine the usefulness of the MEM between the positions of the in-going parts as the input controllable variables and the measured GCs as the outputs. Methods: The data from 34 car frontal cross members (each measured three times) were experimentally collected in a laboratory environment by intentionally changing the positions of the in-going parts, assembling the parts, and subsequently measuring their GCs. A single MEM that completely describes the assembly process was fitted between the GCs and the positions of the in-going parts. Results: We present a modeling technique that can be used to establish which measured GCs are influenced by which controllable variables, and how this occurs. The fitted MEM shows evidence that the variability of some GCs changes over time. The natural variation in the system (i.e., unmodeled variations) is about two times larger than the variation between the assembled cross members. We also present two cases that demonstrate how to use the fitted MEM desirability functions to find corrective or control actions. Conclusion: MEMs are very useful tools for analyzing the assembly processes for car-body parts, which are nonlinear processes with multiple inputs and multiple correlated outputs. MEMs can potentially be applied in numerous industrial processes, since modern manufacturing plants measure all important process variables, which is the sole prerequisite for MEMs applications.
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| 7. |
- Tomasic, Ivan, et al.
(författare)
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Sources of Variation Analysis in Fixtures for Sheet Metal Assembly Process
- 2016
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Ingår i: Swedish Production Symposium 2016 SPS 2016.
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Konferensbidrag (refereegranskat)abstract
- The assembly quality is affected by various factors within which fixture variations are the most important. For that reason significant research on fixture variations has already been done. In this work we propose a linear mixed models (LMMs) application for the purpose of analyzing sources of variation in the fixture Objective: To estimate the strength of influences of different sources of variation on the control and assembly fixtures. The variables considered are: time, operator, default pin positions, shifts from the default pin positions . Methods: The data was collected through assembly and measurement for repeatability and experimental corrective actions. We use LMMs to model the relation between features measured on the assembled parts and the input variables of interest. The LMMs allow taking into account the correlation of observations contained in the dataset. We also use graphical data presentation methods to explore the data. Results: The expected results are the strengths of influences of the individual variables considered, and the pairwise interactions of between the variables, on the assembled parts variations.
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