| 1. |
- Barua, Shaibal, et al.
(författare)
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Automated EEG Artifact Handling with Application in Driver Monitoring
- 2017
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers Inc.. - 2168-2194 .- 2168-2208.
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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, et al.
(författare)
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Automatic driver sleepiness detection using EEG, EOG and contextual information
- 2019
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Ingår i: Expert systems with applications. - : Elsevier Ltd. - 0957-4174 .- 1873-6793. ; 115, s. 121-135
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Tidskriftsartikel (refereegranskat)abstract
- The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepiness classification scheme designed using data from 30 drivers who repeatedly drove in a high-fidelity driving simulator, both in alert and in sleep deprived conditions. Driver sleepiness classification was performed using four separate classifiers: k-nearest neighbours, support vector machines, case-based reasoning, and random forest, where physiological signals and contextual information were used as sleepiness indicators. The subjective Karolinska sleepiness scale (KSS) was used as target value. An extensive evaluation on multiclass and binary classifications was carried out using 10-fold cross-validation and leave-one-out validation. With 10-fold cross-validation, the support vector machine showed better performance than the other classifiers (79% accuracy for multiclass and 93% accuracy for binary classification). The effect of individual differences was also investigated, showing a 10% increase in accuracy when data from the individual being evaluated was included in the training dataset. Overall, the support vector machine was found to be the most stable classifier. The effect of adding contextual information to the physiological features improved the classification accuracy by 4% in multiclass classification and by and 5% in binary classification.
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| 3. |
- Nilsson, Emma, et al.
(författare)
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Vehicle Driver Monitoring : sleepiness and cognitive load
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- To prevent road crashes, it is important to understand driver related contributing factors. The overall aim of the Vehicle Driver Monitoring project was to advance the understanding of two such factors; sleepiness and cognitive distraction. The project aimed at finding methods to measure the two states, with focus on physiological measures, and to study their effect on driver behaviour. The data collection was done in several laboratory and driving simulator experiments. Much new knowledge and insights were gained in the project. Significant effects of cognitive load as well as of sleepiness were found in several physiological measures. The results also showed that context, including individual and environmental factors, has a great impact on driver behaviours, measures and driver experiences.
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| 4. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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A Hybrid Case-Based System in Stress Diagnosis and Treatment
- 2012
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Computer-aided decision support systems play anincreasingly important role in clinical diagnosis and treatment.However, they are difficult to build for domains where thedomain theory is weak and where different experts differ indiagnosis. Stress diagnosis and treatment is an example of such adomain. This paper explores several artificial intelligencemethods and techniques and in particular case-based reasoning,textual information retrieval, rule-based reasoning, and fuzzylogic to enable a more reliable diagnosis and treatment of stress.The proposed hybrid case-based approach has been validated byimplementing a prototype in close collaboration with leadingexperts in stress diagnosis. The obtained sensitivity, specificityand overall accuracy compared to an expert are 92%, 86% and88% respectively.
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| 5. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A vision-based indoor navigation system for individuals with visual impairment
- 2019
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Ingår i: International Journal of Artificial Intelligence. - : CESER Publications. - 0974-0635. ; 17:2, s. 188-201
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Tidskriftsartikel (refereegranskat)abstract
- Navigation and orientation in an indoor environment are a challenging task for visually impaired people. This paper proposes a portable vision-based system to provide support for visually impaired persons in their daily activities. Here, machine learning algorithms are used for obstacle avoidance and object recognition. The system is intended to be used independently, easily and comfortably without taking human help. The system assists in obstacle avoidance using cameras and gives voice message feedback by using a pre-trained YOLO Neural Network for object recognition. In other parts of the system, a floor plane estimation algorithm is proposed for obstacle avoidance and fuzzy logic is used to prioritize the detected objects in a frame and generate alert to the user about possible risks. The system is implemented using the Robot Operating System (ROS) for communication on a Nvidia Jetson TX2 with a ZED stereo camera for depth calculations and headphones for user feedback, with the capability to accommodate different setup of hardware components. The parts of the system give varying results when evaluated and thus in future a large-scale evaluation is needed to implement the system and get it as a commercialized product in this area.
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| 6. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Artificial intelligence, machine learning and reasoning in health informatics—an overview
- 2021
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Ingår i: Intelligent Systems Reference Library, Vol. 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 171-192
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Bokkapitel (refereegranskat)abstract
- As humans are intelligent, to mimic or models of human certain intelligent behavior to a computer or a machine is called Artificial Intelligence (AI). Learning is one of the activities by a human that helps to gain knowledge or skills by studying, practising, being taught, or experiencing something. Machine Learning (ML) is a field of AI that mimics human learning behavior by constructing a set of algorithms that can learn from data, i.e. it is a field of study that gives computers the ability to learn without being explicitly programmed. The reasoning is a set of processes that enable humans to provide a basis for judgment, making decisions, and prediction. Machine Reasoning (MR), is a part of AI evolution towards human-level intelligence or the ability to apply prior knowledge to new situations with adaptation and changes. This book chapter presents some AI, ML and MR techniques and approached those are widely used in health informatics domains. Here, the overview of each technique is discussed to show how they can be applied in the development of a decision support system.
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| 7. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Artificial intelligence, machine learning and reasoning in health informatics—case studies
- 2021
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Ingår i: Intelligent Systems Reference Library, Vol 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 261-291
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Bokkapitel (refereegranskat)abstract
- To apply Artificial Intelligence (AI), Machine Learning (ML) and Machine Reasoning (MR) in health informatics are often challenging as they comprise with multivariate information coming from heterogeneous sources e.g. sensor signals, text, etc. This book chapter presents the research development of AI, ML and MR as applications in health informatics. Five case studies on health informatics have been discussed and presented as (1) advanced Parkinson’s disease, (2) stress management, (3) postoperative pain treatment, (4) driver monitoring, and (5) remote health monitoring. Here, the challenges, solutions, models, results, limitations are discussed with future wishes.
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| 8. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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Big Data Analytics in Health Monitoring at Home
- 2017
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Ingår i: Medicinteknikdagarna 2017 MTD 2017.
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Konferensbidrag (refereegranskat)abstract
- This paper proposed a big data analytics approach applied in the projects ESS-H and E-care@home in the context of biomedical and health informatics with the advancement of information fusion, data abstraction, data mining, knowledge discovery, learning, and reasoning [1][2]. Data are collected through the projects, considering both the health parameters, e.g. temperature, bio-impedance, skin conductance, heart sound, blood pressure, pulse, respiration, weight, BMI, BFP, movement, activity, oxygen saturation, blood glucose, heart rate, medication compliance, ECG, EMG, and EEG, and the environmental parameters e.g. force/pressure, infrared (IR), light/luminosity, photoelectric, room-temperature, room-humidity, electrical usage, water usage, RFID localization and accelerometers. They are collected as semi-structured/unstructured, continuous/periodic, digital/paper record, single/multiple patients, once/several-times, etc. and stored in a central could server [5]. Thus, with the help of embedded system, digital technologies, wireless communication, Internet of Things (IoT) and smart sensors, massive quantities of data (so called ‘Big Data’) with value, volume, velocity, variety, veracity and variability are achieved [2]. The data analysis work in the following three steps. In Step1, pre-processing, future extraction and selection are performed based on a combination of statistical, machine learning and signal processing techniques. A novel strategy to fuse the data at feature level and as well as at data level considers a defined fusion mechanism [3]. In Step2, a combination of potential sequences in the learning and search procedure is investigated. Data mining and knowledge discovery, using the refined data from the above for rule extraction and knowledge mining, with support for anomaly detection, pattern recognition and regression are also explored here [4]. In Step3, adaptation of knowledge representation approaches is achieved by combining different artificial intelligence methods [3] [4]. To provide decision support a hybrid approach is applied utilizing different machine learning algorithms, e.g. case-based reasoning, and clustering [4]. The approach offers several data analytics tasks, e.g. information fusion, anomaly detection, rules and knowledge extraction, clustering, pattern identification, correlation analysis, linear regression, logic regression, decision trees, etc. Thus, the approach assist in decision support, early detection of symptoms, context awareness and patient’s health status in a personal environment.
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| 9. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport
- 2018
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Ingår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225. - Cham : Springer International Publishing. - 9783319762128 ; , s. 101-106
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Konferensbidrag (refereegranskat)abstract
- Improving safer transport includes individual and collective behavioural aspects and their interaction. A system that can monitor and evaluate the human cognitive and physical capacities based on human factor measurement is often beneficial to improve safety in driving condition. However, analysis and evaluation of human factor measurement i.e. Demographics, Behavioural and Physiological in real-time is challenging. This paper presents a methodology for cloud-based data analysis, categorization and metrics correlation in real-time through a H2020 project called SimuSafe. Initial implementation of this methodology shows a step-by-step approach which can handle huge amount of data with variation and verity in the cloud.
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| 10. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Convolutional Neural Network for Driving Maneuver Identification Based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS)
- 2020
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Ingår i: Frontiers in Sustainable Cities. - : Frontiers Media SA. - 2624-9634. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- Identification and translation of different driving manoeuvre are some of the key elements to analysis driving risky behavior. However, the major obstacles to manoeuvre identification are the wide variety of styles of driving manoeuvre which are performed during driving. The objective in this contribution through the paper is to automatic identification of driver manoeuvre e.g. driving in roundabouts, left and right turns, breaks, etc. based on Inertia Measurement Unit (IMU) and Global Positioning System (GPS). Here, several Machine Learning (ML) algorithms i.e. Artificial Neural Network (ANN), Convolutional Neural Network (CNN), K-nearest neighbor (k-NN), Hidden Markov Model (HMM), Random Forest (RF), and Support Vector Machine (SVM) have been applied for automatic feature extraction and classification on the IMU and GPS data sets collected through a Naturalistic Driving Studies (NDS) under an H2020 project called SimuSafe . The CNN is further compared with HMM, RF, ANN, k-NN and SVM to observe the ability to identify a car manoeuvre through roundabouts. According to the results, CNN outperforms (i.e. average F1-score of 0.88 both roundabout and not roundabout) among the other ML classifiers and RF presents better correlation than CNN, i.e. MCC = -.022.
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