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Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (Intelligent Systems)ORCID iD: 0000-0002-7305-7169
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.

This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.

For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%. 

Place, publisher, year, edition, pages
Västerås: Mälardalen University , 2015.
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 217
Keyword [en]
Artificial Intelligent, Intelligent systems, Physiological signal, Driver monitoring
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-28902ISBN: 978-91-7485-231-8 (print)OAI: oai:DiVA.org:mdh-28902DiVA: diva2:853000
Presentation
2015-10-06, Lambda, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Projects
Vehicle Driver Monitoring
Funder
VINNOVA, VDM
Available from: 2015-09-11 Created: 2015-09-10 Last updated: 2015-09-22Bibliographically approved
List of papers
1. Supervised Machine Learning Algorithms to Diagnose Stress for Vehicle Drivers Based on Physiological Sensor Signals
Open this publication in new window or tab >>Supervised Machine Learning Algorithms to Diagnose Stress for Vehicle Drivers Based on Physiological Sensor Signals
2015 (English)In: Studies in Health Technology and Informatics, Volume 211: Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden, 2015, Vol. 211, 241-248 p.Conference paper, (Refereed)
Abstract [en]

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data is difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 211
Keyword
Machine-LearningCase-based reasoningStressPhysiological sensor signal
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-28143 (URN)10.3233/978-1-61499-516-6-241 (DOI)2-s2.0-84939235689 (Scopus ID)978-1-61499-515-9 (ISBN)
Conference
12th International Conference on Wearable Micro and Nano Technologies for Personalized Health pHealth 2015, 2-4 Jun 2015, Västerås, Sweden
Projects
VDM - Vehicle Driver Monitoring
Available from: 2015-06-09 Created: 2015-06-08 Last updated: 2017-01-25Bibliographically approved
2. Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning
Open this publication in new window or tab >>Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning
2014 (English)In: Expert systems with applications, ISSN 0957-4174, Vol. 41, no 2, 295-305 p.Article in journal (Refereed) Published
Abstract [en]

Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Today, clinicians/experts often do the diagnosis of stress, sleepiness and tiredness on the basis of information collected from several physiological sensor signals. Since there are large individual variations when analyzing the sensor measurements and systems with single sensor, they could easily be vulnerable to uncertain noises/interferences in such domain; multiple sensors could provide more robust and reliable decision. Therefore, this paper presents a classification approach i.e. Multivariate Multiscale Entropy Analysis–Case-Based Reasoning (MMSE–CBR) that classifies physiological parameters of wheel loader operators by combining CBR approach with a data level fusion method named Multivariate Multiscale Entropy (MMSE). The MMSE algorithm supports complexity analysis of multivariate biological recordings by aggregating several sensor measurements e.g., Inter-beat-Interval (IBI) and Heart Rate (HR) from Electrocardiogram (ECG), Finger Temperature (FT), Skin Conductance (SC) and Respiration Rate (RR). Here, MMSE has been applied to extract features to formulate a case by fusing a number of physiological signals and the CBR approach is applied to classify the cases by retrieving most similar cases from the case library. Finally, the proposed approach i.e. MMSE–CBR has been evaluated with the data from professional drivers at Volvo Construction Equipment, Sweden. The results demonstrate that the proposed system that fuses information at data level could classify ‘stressed’ and ‘healthy’ subjects 83.33% correctly compare to an expert’s classification. Furthermore, with another data set the achieved accuracy (83.3%) indicates that it could also classify two different conditions ‘adapt’ (training) and ‘sharp’ (real-life driving) for the wheel loader operators. Thus, the new approach of MMSE–CBR could support in classification of operators and may be of interest to researchers developing systems based on information collected from different sensor sources.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-21341 (URN)10.1016/j.eswa.2013.05.068 (DOI)000327279900007 ()2-s2.0-84885957580 (Scopus ID)
Projects
IMod - Intelligent Concentration Monitoring and Warning System for Professional Drivers
Available from: 2013-09-16 Created: 2013-09-11 Last updated: 2017-01-25Bibliographically approved
3. A Review on Machine Learning Algorithms in Handling EEG Artifacts
Open this publication in new window or tab >>A Review on Machine Learning Algorithms in Handling EEG Artifacts
2014 (English)In: The Swedish AI Society (SAIS) Workshop SAIS, 14, 2014Conference paper, (Refereed)
Abstract [en]

Brain waves obtained by Electroencephalograms (EEG) recording are an important research area in medical and health and brain computer interface (BCI). Due to the nature of EEG signal, noises and artifacts can contaminate it, which leads to a serious misinterpretation in EEG signal analysis. These contaminations are referred to as artifacts, which are signals of other than brain activity. Moreover, artifacts can cause significant miscalculation of the EEG measurements that reduces the clinical usefulness of EEG signals. Therefore, artifact handling is one of the cornerstones in EEG signal analysis. This paper provides a review of machine learning algorithms that have been applied in EEG artifacts handling such as artifacts identification and removal. In addition, an analysis of these methods has been reported based on their performance.

Keyword
Electroencephalograms (EEG), Artifacts, Machine Learning
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-26427 (URN)
Conference
The Swedish AI Society (SAIS) Workshop SAIS, 14, 22-23 May 2014, Stockholm, Sweden
Projects
VDM - Vehicle Driver Monitoring
Available from: 2014-11-01 Created: 2014-10-31 Last updated: 2015-10-06Bibliographically approved
4. Clustering based Approach for Automated EEG Artifacts Handling
Open this publication in new window or tab >>Clustering based Approach for Automated EEG Artifacts Handling
2015 (English)In: Frontiers in Artificial Intelligence and Applications, vol. 278, 2015, 7-16 p.Conference paper, (Refereed)
Abstract [en]

Electroencephalogram (EEG), measures the neural activity of the central nervous system, which is widely used in diagnosing brain activity and therefore plays a vital role in clinical and Brain-Computer Interface application. However, analysis of EEG signal is often complex since the signal recoding often contaminates with noises or artifacts such as ocular and muscle artifacts, which could mislead the diagnosis result. Therefore, to identify the artifacts from the EEG signal and handle it in a proper way is becoming an important and interesting research area. This paper presents an automated EEG artifacts handling approach, where it combines Independent Component Analysis (ICA) with a 2nd order clustering approach. Here, the 2nd order clustering approach combines the Hierarchical and Gaussian Picture Model clustering algorithm. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to result, the artifacts in the EEG signals are identified and removed successfully where the clean EEG signal shows acceptable considering visual inspection.

Keyword
Electroencephalogram (EEG), Machine-Learning, Ocular artifacts. Muscle artifacts, Clustering
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-29094 (URN)10.3233/978-1-61499-589-0-7 (DOI)2-s2.0-84963692293 (Scopus ID)9781614995883 (ISBN)
Conference
The 13th Scandinavian Conference on Artificial Intelligence (SCAI 2015), 5-6 November, 2015, Halmstad, Sweden
Projects
VDM - Vehicle Driver Monitoring
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2017-01-25Bibliographically approved
5. Intelligent Automated EEG Artifacts Handling Using Wavelet Transform, Independent Component Analysis and Hierarchical clustering
Open this publication in new window or tab >>Intelligent Automated EEG Artifacts Handling Using Wavelet Transform, Independent Component Analysis and Hierarchical clustering
2015 (English)Conference paper, (Refereed)
Abstract [en]

Billions of interconnected neurons are the building block of human brain. For each brain activity these neurons produce electrical signals or brain waves that can be obtained by the Electroencephalogram (EEG) recording. Due to the characteristics of EEG signal, recorded signal often contaminate with undesired physiological signals other than cerebral signal that refers to as EEG artifacts such as ocular or muscle artifacts. Therefore, identification of artifacts from the EEG signal and handle it in a proper way is becoming an important research area. This paper presents an automated EEG artifacts handling approach, where it combines Wavelet transform, Independent Component Analysis (ICA) with Hierarchical clustering method. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to result, the artifacts in the EEG signals are identified and removed successfully where after handling artifacts EEG signals show acceptable considering visual inspection.

Place, publisher, year, edition, pages
Rome: , 2015
Keyword
Electroencephalogram (EEG), Ocular artifacts. Muscle artifacts, Hierarchical clustering
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-29096 (URN)
Conference
1st Workshop on Embedded Sensor Systems for Health through Internet of Things (ESS-H IoT), 26-27 October, Rome, Italy
Projects
VDM - Vehicle Driver Monitoring
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2017-02-06Bibliographically approved

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Citation style
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  • harvard1
  • ieee
  • modern-language-association-8th-edition
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  • Other locale
More languages
Output format
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