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Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS (Embedded Systems))ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Volvo Construction Equipment, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Örebro University, Sweden. (IS (Embedded Systems))ORCID iD: 0000-0003-3802-4721
2014 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 41, no 2, p. 295-305Article 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.

Place, publisher, year, edition, pages
2014. Vol. 41, no 2, p. 295-305
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-21341DOI: 10.1016/j.eswa.2013.05.068ISI: 000327279900007Scopus ID: 2-s2.0-84885957580OAI: oai:DiVA.org:mdh-21341DiVA, id: diva2:648632
Projects
IMod - Intelligent Concentration Monitoring and Warning System for Professional DriversAvailable from: 2013-09-16 Created: 2013-09-11 Last updated: 2017-12-06Bibliographically 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
Keywords
Artificial Intelligent, Intelligent systems, Physiological signal, Driver monitoring
National Category
Computer Sciences
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: 2018-01-11Bibliographically approved

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Begum, ShahinaBarua, ShaibalAhmed, Mobyen Uddin

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