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Barua, S., Ahmed, M. U., Ahlström, C. & Begum, S. (2019). Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert systems with applications, 115, 121-135
Open this publication in new window or tab >>Automatic driver sleepiness detection using EEG, EOG and contextual information
2019 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 115, p. 121-135Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Contextual information, Driver sleepiness, Electroencephalography, Electrooculography, Machine learning, Accidents, Case based reasoning, Decision trees, Electrophysiology, Fisher information matrix, Learning systems, Nearest neighbor search, Support vector machines, 10-fold cross-validation, Binary classification, Classification accuracy, Individual Differences, Multi-class classification, Physiological features, Classification (of information)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-40526 (URN)10.1016/j.eswa.2018.07.054 (DOI)000448097700009 ()2-s2.0-85051410923 (Scopus ID)
Available from: 2018-08-23 Created: 2018-08-23 Last updated: 2019-01-10Bibliographically approved
Islam, M. R., Barua, S., Ahmed, M. U., Begum, S. & Flumeri, G. D. (2019). Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification. In: The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019: . Paper presented at The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 14 Nov 2019, Rome, Italy.
Open this publication in new window or tab >>Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification
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2019 (English)In: The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

In the pursuit of reducing traffic accidents, drivers' mental workload (MWL) has been considered as one of the vital aspects. To measure MWL in different driving situations Electroencephalography (EEG) of the drivers has been studied intensely. However, in the literature, mostly, manual analytic methods are applied to extract and select features from the EEG signals to quantify drivers' MWL. Nevertheless, the amount of time and effort required to perform prevailing feature extraction techniques leverage the need for automated feature extraction techniques. This work investigates deep learning (DL) algorithm to extract and select features from the EEG signals during naturalistic driving situations. Here, to compare the DL based and traditional feature extraction techniques, a number of classifiers have been deployed. Results have shown that the highest value of area under the curve of the receiver operating characteristic (AUC-ROC) is 0.94, achieved using the features extracted by CNN-AE and support vector machine. Whereas, using the features extracted by the traditional method, the highest value of AUC-ROC is 0.78 with the multi-layer perceptron. Thus, the outcome of this study shows that the automatic feature extraction techniques based on CNN-AE can outperform the manual techniques in terms of classification accuracy.

Keywords
Autoencoder, Convolutional Neural Networks, Electroencephalography, Feature Extraction, Mental Workload
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45059 (URN)
Conference
The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 14 Nov 2019, Rome, Italy
Projects
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved
Islam, M. R., Barua, S., Begum, S. & Ahmed, M. U. (2019). Hypothyroid Disease Diagnosis with Causal Explanation using Case-based Reasoning and Domain-specific Ontology. In: Workshop on CBR in the Health Science WS-HealthCBR: . Paper presented at Workshop on CBR in the Health Science WS-HealthCBR, 09 Sep 2019, Otzenhausen, Germany.
Open this publication in new window or tab >>Hypothyroid Disease Diagnosis with Causal Explanation using Case-based Reasoning and Domain-specific Ontology
2019 (English)In: Workshop on CBR in the Health Science WS-HealthCBR, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Explainability of intelligent systems in health-care domain is still in its initial state. Recently, more efforts are made to leverage machine learning in solving causal inference problems of disease diagnosis, prediction and treatments. This research work presents an ontology based causal inference model for hypothyroid disease diagnosis using case-based reasoning. The effectiveness of the proposed method is demonstrated with an example from hypothyroid disease domain. Here, the domain knowledge is mapped into an ontology and causal inference is performed based on this domain-specific ontology. The goal is to incorporate this causal inference model in traditional case-based reasoning cycle enabling explanation for each solved problem. Finally, a mechanism is defined to deduce explanation for a solution to a problem case from the combined causal statements of similar cases. The initial result shows that case-based reasoning can retrieve relevant cases with 95% accuracy.

Keywords
Case-based Reasoning, Causal Model, Explainability, Explainable Artificial Intelligence, Hypothyroid Diagnosis, Ontology
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45058 (URN)
Conference
Workshop on CBR in the Health Science WS-HealthCBR, 09 Sep 2019, Otzenhausen, Germany
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved
Altarabichi, M. G., Ahmed, M. U. & Begum, S. (2019). Supervised Learning for Road Junctions Identification using IMU. In: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019: . Paper presented at First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019, 20 Mar 2019, Barcelona, Spain.
Open this publication in new window or tab >>Supervised Learning for Road Junctions Identification using IMU
2019 (English)In: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019, 2019Conference paper, Published paper (Refereed)
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-43910 (URN)
Conference
First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019, 20 Mar 2019, Barcelona, Spain
Projects
SimuSafe : Simulator of Behavioural Aspects for Safer Transport
Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-06-17Bibliographically approved
Ahmed, M. U., Begum, S., Catalina, C. A., Limonad, L., Hök, B. & Flumeri, G. D. (2018). Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 101-106).
Open this publication in new window or tab >>Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport
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2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 101-106Conference paper, Published paper (Refereed)
Abstract [en]

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.

Keywords
SimuSafe, safer transport, data-analysis, big data, human factor
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37085 (URN)10.1007/978-3-319-76213-5_14 (DOI)000476922000014 ()2-s2.0-85042536073 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
SimuSafe : Simulator of Behavioural Aspects for Safer Transport
Funder
EU, Horizon 2020, 723386
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2019-08-08Bibliographically approved
Rahman, H., Ahmed, M. U. & Begum, S. (2018). Deep Learning based Person Identification using Facial Images. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 111-115).
Open this publication in new window or tab >>Deep Learning based Person Identification using Facial Images
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 111-115Conference paper, Published paper (Refereed)
Abstract [en]

Person identification is an important task for many applications for example in security. A person can be identified using finger print, vocal sound, facial image or even by DNA test. However, Person identification using facial images is one of the most popular technique which is non-contact and easy to implement and a research hotspot in the field of pattern recognition and machine vision. n this paper, a deep learning based Person identification system is proposed using facial images which shows higher accuracy than another traditional machine learning, i.e. Support Vector Machine.

Keywords
Face recognition, Person Identification, Deep Learning.
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37091 (URN)10.1007/978-3-319-76213-5_17 (DOI)000476922000017 ()2-s2.0-85042545019 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
SafeDriver: A Real Time Driver's State Monitoring and Prediction System
Available from: 2017-10-26 Created: 2017-10-26 Last updated: 2019-08-08Bibliographically approved
Barua, S., Ahmed, M. U. & Begum, S. (2018). Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 26-33).
Open this publication in new window or tab >>Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 26-33Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a distributed data analytics approach for drivers’ mental state monitoring using multivariate physiological signals. Driver’s mental states such as cognitive distraction, sleepiness, stress, etc. can be fatal contributing factors and to prevent car crashes these factors need to be understood. Here, a cloud-based approach with heterogeneous sensor sources that generates extremely large data sets of physiological signals need to be handled and analyzed in a big data scenario. In the proposed physiological big data analytics approach, for driver state monitoring, heterogeneous data coming from multiple sources i.e., multivariate physiological signals are used, processed and analyzed to aware impaired vehicle drivers. Here, in a distributed big data environment, multi-agent case-based reasoning facilitates parallel case similarity matching and handles data that are coming from single and multiple physiological signal sources.

Keywords
Physiological signals, distributed analytics, case-based reasoning
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37076 (URN)10.1007/978-3-319-76213-5_4 (DOI)000476922000004 ()2-s2.0-85042522774 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
VDM - Vehicle Driver Monitoring
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2019-08-08Bibliographically approved
Ahmed, M. U., Rahman, H. & Begum, S. (2018). Quality index analysis on camera- A sed R-eak identification considering movements and light illumination. In: Studies in Health Technology and Informatics, vol 249: . Paper presented at 15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018 (pp. 84-92). IOS Press
Open this publication in new window or tab >>Quality index analysis on camera- A sed R-eak identification considering movements and light illumination
2018 (English)In: Studies in Health Technology and Informatics, vol 249, IOS Press , 2018, p. 84-92Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: Normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ±200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men. 

Place, publisher, year, edition, pages
IOS Press, 2018
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-40196 (URN)10.3233/978-1-61499-868-6-84 (DOI)2-s2.0-85049018248 (Scopus ID)9781614998679 (ISBN)
Conference
15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018
Available from: 2018-07-05 Created: 2018-07-05 Last updated: 2018-07-05Bibliographically approved
Ahmed, M. U., Rahman, H. & Begum, S. (2018). Quality Index Analysis on Camera-based R-peak Identification Considering Movements and Light Illumination. In: 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018: . Paper presented at 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018, 12 Jun 2018, Gjövik, Norway.
Open this publication in new window or tab >>Quality Index Analysis on Camera-based R-peak Identification Considering Movements and Light Illumination
2018 (English)In: 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018, 2018Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ?200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men.

Keywords
Quality Index Analysis, Camera-based system, R-peak Identification
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-39258 (URN)
Conference
15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018, 12 Jun 2018, Gjövik, Norway
Projects
HR R-peak detection quality index analysis
Available from: 2018-05-23 Created: 2018-05-23 Last updated: 2018-05-23Bibliographically approved
Barua, S., Begum, S. & Ahmed, M. U. (2018). Scalable Framework for Distributed Case-based Reasoning for Big data analytics. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 111-114).
Open this publication in new window or tab >>Scalable Framework for Distributed Case-based Reasoning for Big data analytics
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 111-114Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a scalable framework for distributed case-based reasoning methodology to provide actionable knowledge based on historical big amount of data. The framework addresses several challenges, i.e., promptly analyse big data, cross-domain, use-case specific data processing, multi-source case representation, dynamic case-management, uncertainty, check the plausibility of solution after adaptation etc. through its’ five modules architectures. The architecture allows the functionalities with distributed data analytics and intended to provide solutions under different conditions, i.e. data size, velocity, variety etc.

Keywords
Distributed analytics, case-based reasoning
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37087 (URN)10.1007/978-3-319-76213-5_16 (DOI)000476922000016 ()2-s2.0-85042527811 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
VDM - Vehicle Driver Monitoring
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2019-08-08Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1212-7637

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