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Supervised Machine Learning Algorithms to Diagnose Stress for Vehicle Drivers Based on Physiological Sensor Signals
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
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, Published 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.

Place, publisher, year, edition, pages
2015. Vol. 211, 241-248 p.
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 211
Keyword [en]
Machine-LearningCase-based reasoningStressPhysiological sensor signal
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-28143DOI: 10.3233/978-1-61499-516-6-241Scopus ID: 2-s2.0-84939235689ISBN: 978-1-61499-515-9 (print)OAI: oai:DiVA.org:mdh-28143DiVA: diva2:818741
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
In thesis
1. Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
Open this publication in new window or tab >>Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
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
Artificial Intelligent, Intelligent systems, Physiological signal, Driver monitoring
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-28902 (URN)978-91-7485-231-8 (ISBN)
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

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Citation style
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