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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)2-s2.0-85051410923 (Scopus ID)
Available from: 2018-08-23 Created: 2018-08-23 Last updated: 2018-08-23Bibliographically 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)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
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2018-03-08Bibliographically 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)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: 2018-03-08Bibliographically 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)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: 2018-03-08Bibliographically 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)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: 2018-03-08Bibliographically approved
Barua, S., Begum, S. & Ahmed, M. U. (2018). Towards Distributed k-NN similarity for Scalable Case Retrieval. In: ICCBR 2018: The 26th International Conference on Case-Based Reasoning July, 09th-12th 2018 in Stockholm, Sweden, Workshop Proceedings. Paper presented at XCBR: First Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems. Workshop at the 26th International Conference on Case-Based Reasoning (ICCBR 2018) (pp. 151-160).
Open this publication in new window or tab >>Towards Distributed k-NN similarity for Scalable Case Retrieval
2018 (English)In: ICCBR 2018: The 26th International Conference on Case-Based Reasoning July, 09th-12th 2018 in Stockholm, Sweden, Workshop Proceedings, 2018, p. 151-160Conference paper, Published paper (Refereed)
Abstract [en]

In Big data era, the demand of processing large amount of data posing several challenges. One biggest challenge is that it is no longer possible to process the data in a single machine. Similar challenges can be assumed for case-based reasoning (CBR) approach, where the size of a case library is increasing and constructed using heterogenous data sources. To deal with the challenges of big data in CBR, a distributed CBR system can be developed, where case libraries or cases are distributed over clusters. MapReduce programming framework has the facilities of parallel processing massive amount of data through a distributed system. This paper proposes a scalable case-representation and retrieval approach using distributed k-NN similarity. The proposed approach is considered to be developed using MapReduce programming framework, where cases are distributed in many clusters.

Keywords
Case representation, k-NN similarity, Scalable Case Retrieval, distributed CBR, big data
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-40882 (URN)
Conference
XCBR: First Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems. Workshop at the 26th International Conference on Case-Based Reasoning (ICCBR 2018)
Available from: 2018-09-18 Created: 2018-09-18 Last updated: 2018-09-18Bibliographically approved
Rahman, H., Ahmed, M. U. & Begum, S. (2018). Vision-Based Remote Heart Rate Variability Monitoring using Camera. 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. 10-18).
Open this publication in new window or tab >>Vision-Based Remote Heart Rate Variability Monitoring using Camera
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 10-18Conference paper, Published paper (Refereed)
Abstract [en]

Heart Rate Variability (HRV) is one of the important physiological parameter which is used to early detect many fatal disease. In this paper a non-contact remote Heart Rate Variability (HRV) monitoring system is developed using the facial video based on color variation of facial skin caused by cardiac pulse. The lab color space of the facial video is used to extract color values of skin and signal processing algorithms i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA), Principle Component Analysis (PCA) are applied to monitor HRV. First, R peak is detected from the color variation of skin and then Inter-Beat-Interval (IBI) is calculated for every consecutive R-R peak. HRV features are then calculated based on IBI both in time and frequency domain. MySQL and PHP programming language is used to store, monitor and display HRV parameters remotely. In this study, HRV is quantified and compared with a reference measurement where a high degree of similarities is achieved. This technology has significant potential for advancing personal health care especially for telemedicine.

Keywords
Physiological signals, Heart Rate, Inter-beat-Interval, Heart-Rate-Variability, Non-contact, Remote Monitoring.
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37072 (URN)10.1007/978-3-319-76213-5_2 (DOI)2-s2.0-85042538568 (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: 2017-10-31 Created: 2017-10-31 Last updated: 2018-03-08Bibliographically approved
Barua, S., Ahmed, M. U., Ahlström, C., Begum, S. & Funk, P. (2017). Automated EEG Artifact Handling with Application in Driver Monitoring. IEEE journal of biomedical and health informatics, PP(99)
Open this publication in new window or tab >>Automated EEG Artifact Handling with Application in Driver Monitoring
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2017 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. PP, no 99Article in journal (Refereed) Epub ahead of print
Abstract [en]

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 preprocessing 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.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Artifacts, Clustering, Electroencephalogram, Independent Component Analysis, Wavelet decomposition
National Category
Signal Processing
Identifiers
urn:nbn:se:mdh:diva-37347 (URN)10.1109/JBHI.2017.2773999 (DOI)000441795800003 ()2-s2.0-85035807991 (Scopus ID)
Projects
VDM - Vehicle Driver MonitoringSafeDriver: A Real Time Driver's State Monitoring and Prediction System
Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-09-06Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1212-7637

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