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  • 51.
    Altarabichi, Mohammed Ghaith
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ciceri, Maria Rita
    Università Cattolica del Sacro Cuore di Milano, Italy.
    Balzarotti, Stefania
    Università Cattolica del Sacro Cuore di Milano, Italy.
    Biassoni, Federica
    Università Cattolica del Sacro Cuore di Milano, Italy.
    Lombardi, Debora
    Università Cattolica del Sacro Cuore di Milano, Italy.
    Perego, Paolo
    Università Cattolica del Sacro Cuore di Milano, Italy.
    Reaction Time Variability Association with Unsafe Driving2020In: Transport Research Arena TRA2020, Helsinki, Finland, 2020Conference paper (Refereed)
    Abstract [en]

    This paper investigates several human factors including visual field, reaction speed, driving behavior and personality traits based on results of a cognitive assessment test targeting drivers in a Naturalistic Driving Study (NDS). Frequency of being involved in Near Miss event (fnm) and Frequency of committing Traffic Violation (ftv) are defined as indexes of safe driving in this work. Inference of association shows statistically significant correlation between Standard Deviation of Reaction Time (σRT) and both safe driving indexes fnm and ftv. Causal relationship analysis excludes age as confounding factor as variations in behavioral responses is observed in both younger and older drivers of this study.

  • 52.
    Banaee, Hadi
    et al.
    Örebro University, Sweden.
    Ahmed, Mobyen Uddin
    Örebro University, Sweden.
    Loutfi, Amy
    Örebro University, Sweden.
    A framework for automatic text generation of trends in physiological time series data2013In: Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, 2013, p. 3876-3881Conference paper (Refereed)
    Abstract [en]

    Health monitoring systems using wearable sensors have rapidly grown in the biomedical community. The main challenges in physiological data monitoring are to analyse large volumes of health measurements and to represent the acquired information. Natural language generation is an effective method to create summaries for both clinicians and patients as it can describe useful information extracted from sensor data in textual format. This paper presents a framework of a natural language generation system that provides a text-based representation of the extracted numeric information from physiological sensor signals. More specifically, a new partial trend detection algorithm is introduced to capture the particular changes and events of health parameters. The extracted information is then represented considering linguistic characterisation of numeric features. Experimental analysis was performed using a wearable sensor and demonstrates a possible output in natural language text.

  • 53.
    Banaee, Hadi
    et al.
    Örebro University, Sweden.
    Ahmed, Mobyen Uddin
    Örebro University, Sweden.
    Loutfi, Amy
    Örebro University, Sweden.
    Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges2013In: Sensors, ISSN 1424-8220, Vol. 13, no 12, p. 17472-17500Article in journal (Refereed)
    Abstract [en]

    The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

  • 54.
    Banaee, Hadi
    et al.
    Örebro University, Sweden.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Örebro University, Sweden.
    Loutfi, Amy
    Örebro University, Sweden.
    Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data2015In: 8th International Conference on Health Informatics HEALTHINF, Lisbon, Portugal, 2015Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to automatically mine rules in time series data representing physiological parameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined for each physiological time series data. The generated rules based on the prototypical patterns are then described in a textual representation which captures trends in each physiological parameter and their relation to the other physiological data. In this paper, a method for measuring similarity of rule sets is introduced in order to validate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classes in the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rule mining technique is able to acquire a distinctive model for each clinical condition, and represent the generated rules in a human understandable textual representation.

  • 55.
    Banaee, Hadi
    et al.
    Örebro University, Sweden.
    Ahmed, Mobyen Uddin
    Örebro University, Sweden.
    Loutfi, Amy
    Örebro University, Sweden.
    Towards NLG for Physiological Data Monitoring with Body Area Networks2013In: 14th European Workshop on Natural Language Generation ENLG, London, United Kingdom, 2013Conference paper (Refereed)
    Abstract [en]

    This position paper presents an on-going work on a natural language generation framework that is particularly tailored for summary text generation from body area networks. We present an overview of the main challenges when considering this type of sensor devices used for at home monitoring of health parameters. This pa- per describes the first steps towards the im- plementation of a system which collects information from heart rate and respira- tion rate using a wearable sensor. The pa- per further outlines the direction for future work and in particular the challenges for NLG in this application domain

  • 56.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlström, Christer
    The Swedish National Road and Transport Research Institute (VTI), Linköping, SE, Sweden.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Automatic driver sleepiness detection using EEG, EOG and contextual information2019In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 115, p. 121-135Article in journal (Refereed)
    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.

  • 57.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlström, Christer
    MFT, Linköping Sweden.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Automated EEG Artifact Handling with Application in Driver Monitoring2017In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 5, p. 1350-1361Article in journal (Refereed)
    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.

  • 58.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Classifying drivers' cognitive load using EEG signals2017In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 237, p. 99-106Article in journal (Refereed)
    Abstract [en]

    A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy. 

  • 59.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring2018In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 26-33Conference 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.

  • 60.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual dataIn: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019Conference paper (Refereed)
    Abstract [en]

    Analysing physiological parameters together with contextual information of car drivers to identify drivers’ sleepiness is a challenging issue. Machine learning algorithms show high potential in data analysis and classification tasks in many domains. This paper presents a use case of machine learning approach for drivers’ sleepiness classification. The classifications are conducted based on drivers’ physiological parameters and contextual information. The sleepiness classification shows receiver operating characteristic (ROC) curves for KNN, SVM and RF were 0.98 on 10-fold cross-validation and 0.93 for leave-one-out (LOO) for all classifiers.

  • 61.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Clustering based Approach for Automated EEG Artifacts Handling2015In: Frontiers in Artificial Intelligence and Applications, vol. 278, 2015, p. 7-16Conference 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.

  • 62.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Driver’s State Monitoring: A Case Study on Big Data Analytics2016In: The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 2016, Vol. 187, p. 145-147Conference paper (Refereed)
    Abstract [en]

    Driver's distraction, inattention, sleepiness, stress, etc. are identified as causal factors of vehicle crashes and accidents. Today, we know that physiological signals are convenient and reliable measures of driver’s impairments. Heterogeneous sensors are generating vast amount of signals, which need to be handled and analyzed in a big data scenario. Here, we propose a big data analytics approach for driver state monitoring using heterogeneous data that are coming from multiple sources, i.e., physiological signals along with vehicular data and contextual information. These data are processed and analyzed to aware impaired vehicle drivers.

  • 63.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Intelligent automated eeg artifacts handling using wavelet transform, independent component analysis and hierarchal clustering2017In: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng., Springer Verlag , 2017, p. 144-148Conference paper (Refereed)
    Abstract [en]

    Billions of interconnected neurons are the building block of the 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 signals, recorded signals often contaminate with undesired physiological signals other than the cerebral signal that is referred to as the EEG artifacts such as the ocular or the muscle artifacts. Therefore, identification and handling of artifacts in the EEG signals in a proper way is becoming an important research area. This paper presents an automated EEG artifacts handling approach, combining Wavelet transform, Independent Component Analysis (ICA), and Hierarchical clustering. The effectiveness of the proposed approach has been examined and observed on real EEG recording. According to the result, the proposed approach identified artifacts in the EEG signals effectively and after handling artifacts EEG signals showed acceptable considering visual inspection. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

  • 64.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Intelligent Automated EEG Artifacts Handling Using Wavelet Transform, Independent Component Analysis and Hierarchical clustering2015Conference 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.

  • 65.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Scalable Framework for Distributed Case-based Reasoning for Big data analytics2018In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 111-114Conference 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.

  • 66.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Supervised Machine Learning Algorithms to Diagnose Stress for Vehicle Drivers Based on Physiological Sensor Signals2015In: 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, p. 241-248Conference 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.

  • 67.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Towards Distributed k-NN similarity for Scalable Case Retrieval2018In: ICCBR 2018: The 26th International Conference on Case-Based Reasoning July, 09th-12th 2018 in Stockholm, Sweden, Workshop Proceedings, 2018, p. 151-160Conference 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.

  • 68.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlström, Christer
    The Swedish National Road and Transport Research Institute (VTI), Sweden.
    AUTOMATED EEG ARTIFACTS HANDLING FOR DRIVER SLEEPINESS MONITORING2016In: 2nd International Symposium on Somnolence, Vigilance, and Safety SomnoSafe2016, 2016Conference paper (Refereed)
  • 69.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Barua, Shaibal
    Multi-Scale Entropy Analysis and Case-Based Reasoning to Classify Physiological Sensor Signals2012In: Proceedings of the ICCBR 2012 Workshops / [ed] Luc Lamontagne and Juan A. Recio-García, 2012, p. 129-138Conference paper (Refereed)
    Abstract [en]

    Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Clinicians/experts often do the diagnosis of stress, sleepiness, tiredness etc. based on several physiological sensor signals to achieve better accuracy in classification. This paper presents a case-based reasoning (CBR) system that offers an opportunity to classify healthy and stressed persons based on sensor signal fusion. Several sensor measurements for instance, i.e., heart rate, inter-beat-interval, finger temperature, skin conductance and respiration rate have been combined for the data level fusion using Multivariate Multiscale Entropy Analysis (MMSE) algorithm. This algorithm supports complexity analysis of multivariate biological recordings. Here, MMSE is used to formulate cases in the case-based classification system.

  • 70.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Case-Based Systems in the Health Sciences - A Case Study in the Field of Stress Management2009In: WSEAS Transactions on Systems, ISSN 1109-2777, Vol. 8, no 3, p. 344-354Article in journal (Refereed)
    Abstract [en]

    Now-a-days medical domain is a popular area for the artificial intelligence (AI) research. Many of the early AI systems were attempted to apply rule-based reasoning in developing computer-based diagnosis system in medical domain. However, for a broad and complex medical domain the effort of applying rule-based system has encountered several problems. Today many systems are serving multi-purpose i.e. tend to support not only in diagnosis but also in number of other complex tasks and combining more than one AI techniques in the health care domain. In this paper, we will investigate the state-of-the art of casebased reasoning (CBR), a recent AI method in the medical domain. A case study in the stress medicine domain is presented here. Today stress has become a major concern in our society. The demand of the decision support system (DSS) in stress domain is increasing rapidly. However, the application of DSS in this domain is limited so far due to the weak domain theory. In our on going research, we have proposed a solution analyzing the relation between stress and finger temperature using case-based reasoning and other AI techniques namely case-based reasoning, textual CBR, rule-based reasoning, and fuzzy logic to support classification and diagnosis in stress management.

  • 71.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    ECG Sensor Signal Analysis to Represent Cases in a Case-based Stress Diagnosis System2010In: Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB, Corfu, Greece, 2010Conference paper (Refereed)
    Abstract [en]

    This paper presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) sensor signal. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress using ECG sensor signal.

  • 72.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Physiological Sensor Signals Analysis to Represent Cases in a Case-based Diagnostic System2013In: Innovations in Knowledge-based Systems in Biomedicine, vol. 250 / [ed] Pham T.D,Jain L.C., Springer, 2013, p. 1-25Chapter in book (Other academic)
    Abstract [en]

    Today, medical knowledge is expanding so rapidly that clinicians cannot follow all progress any more. This is one reason for making knowledge- based systems desirable in medicine. Such systems can give a clinician a second opinion and give them access to new experience and knowledge. Recent advances in Artificial Intelligence (AI) offers methods and techniques with the potential of solving tasks previously difficult to solve with computer-based systems in medical domains. This chapter is especially concerned with diagnosis of stress-related dysfunctions using AI methods and techniques. Since there are large individual variations between people when looking at biological sensor signals to diagnose stress, this is a worthy challenge. Stress is an inevitable part of our human life. No one can live without stress. However, long-term exposure to stress may in the worst case cause severe mental and/or physical problems that are often related to different kind of psychosomatic disorders, coronary heart disease etc. So, diagnosis of stress is an important issue for health and well-being. Diagnosis of stress often involves acquisition of biological signals for example finger temperature, electrocardiogram (ECG), electromyography (EMG) signal, skin conductance (SC) signals etc. and is followed by a careful analysis by an expert. However, the number of experts to diagnose stress in psycho-physiological domain is limited. Again, responses to stress are different for different persons. So, interpreting a particular curve and diagnosing stress levels is difficult even for experts in the domain due to large individual variations. It is a highly complex and partly intuitive process which experienced clinicians use when manually inspecting biological sensor signals and classifying a patient. Clinical studies show that the pattern of variation within heart rate i.e., HRV signal and finger temperature can help to determine stress-related disorders. This chapter presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) and finger temperature sensor signals. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress.

  • 73.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Filla, Reno
    Driver's Mental State Monitoring System Using CBR Based on Heart Rate Variability Analysis2012In:  , 2012Conference paper (Refereed)
    Abstract [en]

    The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. This is especially important for professional drivers since both expensive equipment and lives may be at stake, e.g. in mining, construction and personal transportation, reduced concentration, stress or tiredness are known to be the cause of many accidents. A system which recognizes the state of the driver and e.g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. Today different sensors enable clinician to determine a driver’s status with high accuracy. The aim of the paper is to develop an intelligent system that can monitor drivers’ stress depending on psychological and behavioral conditions/status using heart rate variability. An experienced clinician is able to diagnose a person’s stress level based on sensor readings. Here, we propose a solution using case-based reasoning to diagnose individual driver’s stress. During calibration a number of individual parameters are established. The system also considers the feedback from the driver’s on how well the test was performed The validation of the approach is based on close collaboration with experts and measurements from 18 driver’s from Volvo Construction Equipment are used as reference.

  • 74.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Filla, Reno
    Volvo.
    Mental State Monitoring System for the Professional Drivers Based on Heart Rate Variability Analysis and Case-based Reasoning2012In: 2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), NEW YORK: IEEE , 2012, p. 35-42Conference paper (Refereed)
    Abstract [en]

    The consequences of tiredness, drowsiness, stress and lack of concentration caused by a variety of different factors such as illness, sleep depletion, drugs and alcohol is a serious problem in traffic and when operating industrial equipment. A system that recognizes the state of the driver and e. g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. So, the aim of the project is to develop an intelligent system that can monitor drivers' stress depending on psychological and behavioral conditions/status using Heart Rate Variability (HRV). Here, we have proposed a solution using Case-Based Reasoning (CBR) to diagnose individual driver's level of stress. The system also considers feedback from the driver's on how well the test was performed. The validation of the approach is based on close collaboration with experts and measurements from 18 drivers from Volvo Construction Equipment (Volvo CE) are used as reference.

  • 75.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering.
    An Overview on Recent Case-Based Reasoning Systems in the Medicine2009In: 25th annual workshop of the Swedish Artificial Intelligence Society, 2009Conference paper (Refereed)
    Abstract [en]

    Case-based reasoning systems for medical application are increasingly applied to meet the challenges from the medical domain. This paper looks at the state of the art in case-based reasoning and some systems are classified in this respect. A survey is performed based on the recent publications and research projects in CBR in medicine. Also, the survey is based on e-mail questionnaire to the authors’ to complete the missing property information. Some clear trends in recent projects/systems have been identified such as most of the systems are multi-modal, using a variety of different methods and techniques to serve multipurpose i.e. address more than one task.

  • 76.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering.
    Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments2011In: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews, ISSN 1094-6977, E-ISSN 1558-2442, Vol. 41, no 4, p. 421-434Article in journal (Refereed)
    Abstract [en]

    The Health Sciences are, nowadays, one of the major application areas for case-based reasoning (CBR). The paper presents a survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented. Some clear trends have been identified, such as multipurpose systems: more than half of the current medical CBR systems address more than one task. Research on CBR in the area is growing, but most of the systems are still prototypes and not available on the market as commercial products. However, many of the projects/systems are intended to be commercialized.

  • 77.
    Begum, Shahina
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Ahmed, Mobyen Uddin
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    Folke, Mia
    Mälardalen University, Department of Computer Science and Electronics.
    von Schéele, Bo
    Mälardalen University, Department of Computer Science and Electronics.
    A computer-based system for the assessment and diagnosis of individual sensitivity to stress in Psychophysiology2007Conference paper (Refereed)
    Abstract [en]

    Increased exposure to stress may cause serious health problems leading to long term sick leave if undiagnosed and untreated. The practice amongst clinicians' to use a standardized procedure measuring blood pressure, ECG, finger temperature, breathing speed etc. to make a reliable diagnosis of stress and stress sensitivity is increasing. But even with these measurements it is still difficult to diagnose due to large individual variations. A computer-based system as a second option for the assessment and diagnosis of individual stress level is valuable in this domain.

    A combined approach based on a calibration phase and case-based reasoning is proposed exploiting data from finger temperature sensor readings from 24 individuals. In calibration phase, a standard clinical procedure with six different steps helps to establish a person's stress profile and set up a number of individual parameters. When acquiring a new case, patients are also asked to provide a fuzzy evaluation on how reliable was the procedure to define the case itself. Such a reliability "level" could be used to further discriminate among similar cases. The system extracts key features from the signal and classifies individual sensitivity to stress. These features are stored into a case library and similarity measurements are taken to assess the degrees of matching and create a ranked list containing the most similar cases retrieved by using the nearest-neighbor algorithm.

    A current case (CC) is compared with two other stored cases (C_92 and C_115) in the case library. The global similarity between the case CC and case C_92 is 67% and case CC and case C_115 is 80% shown by the system. So the case C_115 has ranked higher than the case C_92 and is more similar to current case CC. If necessary, the solution for the best matching case can be revised by the clinician to fit the new patient. The current problem with confirmed solution is then retained as a new case and added to the case library for future use.

    The system allows us to utilize previous experience and at the same time diagnose stress along with a stress sensitivity profile. This information enables the clinician to make a more informed decision of treatment plan for the patients. Such a system may also be used to actively notify a person's stress levels even in the home environment.

  • 78.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering.
    Schéele, Bo von
    Mälardalen University, School of Innovation, Design and Engineering.
    A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching2009In: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 25, no 3, p. 180-195Article in journal (Refereed)
    Abstract [en]

    Stress diagnosis based on finger temperature signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret and analyze complex, lengthy sequential measurements in order to make a diagnosis and treatment plan. The paper presents a case-based decision support system to assist clinicians in performing such tasks. Case-based reasoning is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness-of-fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation which shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with case-based reasoning is a valuable approach in domains where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho-physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.

  • 79.
    Begum, Shahina
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Ahmed, Mobyen Uddin
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    von Schéele, Bo
    Mälardalen University, Department of Computer Science and Electronics.
    Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning2007In: Case-Based Reasoning Research and Development: 7th International Conference on Case-Based Reasoning, ICCBR 2007 Belfast, Northern Ireland, UK, August 13-16, 2007 Proceedings, Springer, 2007, p. 478-491Chapter in book (Refereed)
    Abstract [en]

    Increased exposure to stress may cause health problems. An experi-enced clinician is able to diagnose a person's stress level based on sensor read-ings. Large individual variations and absence of general rules make it difficult to diagnose stress and the risk of stress-related health problems. A decision sup-port system providing clinicians with a second opinion would be valuable. We propose a novel solution combining case-based reasoning and fuzzy logic along with a calibration phase to diagnose individual stress. During calibration a num-ber of individual parameters are established. The system also considers the feedback from the patient on how well the test was performed. The system uses fuzzy logic to incorporating the imprecise characteristics of the domain. The cases are also used for the individual treatment process and transfer experience between clinicians. The validation of the approach is based on close collabora-tion with experts and measurements from 24 persons used as reference.

  • 80.
    Begum, Shahina
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Ahmed, Mobyen Uddin
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    von Schéele, Bo
    Mälardalen University, Department of Computer Science and Electronics.
    Individualized Stress Diagnosis Using Calibration and Case-Based Reasoning2007In: Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society, Borås, Sweden, 2007, p. 59-69Conference paper (Refereed)
    Abstract [en]

    Diagnosing stress is difficult even for experts due to large individual variations. Clinician's use today manual test procedures where they measure blood pressure, ECG, finger temperature and breathing speed during a number of exercises. An experienced clinician makes diagnosis on different readings shown in a computer screen. There are only very few experts who are able to diagnose and predict stress-related problems. In this paper we have proposed a combined approach based on a calibration phase and case-based reasoning to provide assistance in diagnosing stress, using data from the finger temperature sensor readings. The calibration phase helps to establish a number of individual parameters. The system uses a case-based reasoning approach and also feedback on how well the patient succeeded with the different test, used for giving similar cases reliability estimates.

  • 81.
    Begum, Shahina
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Ahmed, Mobyen Uddin
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    von Schéele, Bo
    Mälardalen University, Department of Computer Science and Electronics.
    Similarity of Medical Cases in Health Care Using Cosine Similarity and Ontology2007Conference paper (Refereed)
    Abstract [en]

    The increasing use of digital patient records in hospital saves both time and reduces risks wrong treatments caused by lack of information. Digital patient records also enable efficient spread and transfer of experience gained from diagnosis and treatment of individual patient. This is today mostly manual (speaking with col-leagues) and rarely aided by computerized system. Most of the content in patient re-cords is semi-structured textual information. In this paper we propose a hybrid tex-tual case-based reasoning system promoting experience reuse based on structured or unstructured patient records, case-based reasoning and similarity measurement based on cosine similarity metric improved by a domain specific ontology and the nearest neighbor method. Not only new cases are learned, hospital staff can also add comments to existing cases and the approach enables prototypical cases.

  • 82.
    Begum, Shahina
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Ahmed, Mobyen Uddin
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    von Schéele, Bo
    Mälardalen University, Department of Computer Science and Electronics.
    Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress2006In: 8th European Workshop on Case-based Reasoning in the Health Sciences, workshop proceedings, 2006, p. 113-122Conference paper (Refereed)
    Abstract [en]

    In the medical literature there are a number of physiological reactions related to cognitive activities. Psychosocial and psychophysiological stress is such activities reflected in physiological reactions. Stress related symptoms are highly individual, but decreased hands temperature is the common for most individuals. A clinician learns with experience how to interpret the different symptoms but there is no adaptive diagnostic system for diagnosing stress. Decision support systems (DSS) diagnosing stress would be valuable both for junior clinicians and as second opinion for experts. Due to the large individual variations and no general set of rules, DSS are difficult to build for this task. The proposed solution combines a calibration phase with case-based reason¬ing approach and fuzzification of cases. During the calibration phase a number of individual parameters and case specific fuzzy membership functions are es-tablishes. This case-based approach may help the clinician to make a diagnosis, classification and treatment plan. The case may also be used to follow the treat-ment progress. This may be done using the proposed system. Initial tests show promising results. The individual cases including calibration and fuzzy mem-bership functions may also be used in an autonomous system in home environ-ment for treatment programs for individuals often under high stress.

  • 83.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering.
    von Schéele, Bo
    Mälardalen University, School of Innovation, Design and Engineering.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering.
    Diagnosis and Biofeedback System for Stress2009In: Proceedings of the 6th International Workshop on Wearable, Micro, and Nano Technologies for Personalized Health: "Facing Future Healthcare Needs", pHealth 2009, 2009, p. 17-20Conference paper (Refereed)
    Abstract [en]

    Today, everyday life for many people contain many situations that may trigger stress or result in an individual living on an increased stress level under long time. High level of stress may cause serious health problems. It is known that respiratory rate is an important factor and can be used in diagnosis and biofeedback training, but available measurement of respiratory rate are not especially suitable for home and office use. The aim of this project is to develop a portable sensor system that can measure the stress level, during everyday situations e.g. at home and in work environment and can help the person to change the behaviour and decrease the stress level. The sensor explored is a finger temperature sensor. Clinical studies show that finger temperature, in general, decreases with stress; however this change pattern shows large individual variations. Diagnosing stress level from the finger temperature is difficult even for clinical experts. Therefore a computer-based stress diagnosis system is important. In this system, case-based reasoning and fuzzy logic have been applied to assists in stress diagnosis and biofeedback treatment utilizing the finger temperature sensor signal. An evaluation of the system with an expert in stress diagnosis shows promising result.

  • 84.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    von Schéele, Bo
    Mälardalen University, School of Innovation, Design and Engineering.
    Olsson, Erik
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Development of a Stress Questionnaire: A Tool for Diagnosing Mental Stress2010Report (Other academic)
    Abstract [en]

    Stress and its relation with health, behavioral and environmental factors are known today. The stress questionnaire is a scientific screening instrument to understand individual’s causes of stress in different parts of life e.g. in the work place and at home. The 38-item stress questionnaire (SQ) is developed to assess the appraisal of stress personally experienced in a patient’s life. This questionnaire cannot diagnose any illness or psychological disorder. However it can be a helpful tool for developing the individual stress management plan by assessing data about the current demands of individual’s life and work.

  • 85.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management2010In: Computational Intelligence in Healthcare 4: Advanced Methodologies / [ed] Isabelle Bichindaritz et. al., Springer Berlin/Heidelberg, 2010, p. 159-189Chapter in book (Other academic)
    Abstract [en]

    The complexity of modern lifestyle and society brings many advantages but also causes increased levels of stress for many people. It is known that increased exposure to stress may cause serious health problems if undiagnosed and untreated and a report from the Swedish government estimates that 1/3 of all long term sick leave is stress related. One of the physiological parameters for quantifying stress levels is the finger temperature that helps the clinician in diagnosis and treatment of stress. However, in practice, the complex and varying nature of signals makes it difficult and tedious to interpret and analyze the lengthy sequential measurements. A computer based system diagnosing stress would be valuable both for clinicians and for treatment. Since stress diagnosis has a week domain theory and there are large individual variations, Case-Based Reasoning is proposed as the main methodology. Feature extraction methods abstracting the original signals without losing key features are investigated. A fuzzy technique is also incorporated into the system to perform matching between the features derived from signals to better accommodate vagueness, uncertainty often present in clinical reasoning Validation of the approach is based on close collaboration with experts and measurements from twenty four persons. The system formulates a new problem case with 17 extracted features from the fifteen minutes (1800 samples) of biomedical sensor data. Thirty nine time series from twenty four persons have been used to evaluate the approach (matching algorithms) in which the system shows a level of performance close to an experienced expert. The system can be used as an expert for a less experienced clinician, as a second option for an experienced clinician or for treatment in home environment.

  • 86.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    In-Vehicle Stress Monitoring Based on EEG Signal2017In: International Journal of Engineering Research and Applications, ISSN 2248-9622, E-ISSN 2248-9622, Vol. 7, no 7, p. 55-71Article in journal (Refereed)
    Abstract [en]

    In recent years, improved road safety by monitoring human factors i.e., stress, mental load, sleepiness, fatigue etc. of vehicle drivers has been addressed in a number of studies. Due to the individual variations and complex dynamic in-vehicle environment systems that can monitor such factors of a driver while driving is challenging. This paper presents a drivers’ stress monitoring system based on electroencephalography (EEG) signals enabling individual-focused computational approach that can generate automatic decision. Here, a combination of different signal processing i.e., discrete wavelet transform, largest Lyapunov exponent (LLE) and modified covariance have been applied to extract key features from the EEG signals. Hybrid classification approach Fuzzy-CBR (case-based reasoning) is used for decision support. The study has focused on both long and short-term temporal assessment of EEG signals enabling monitoring in different time intervals. In short time interval, which requires complex computations, the classification accuracy using the proposed approach is 79% compare to a human expert. Accuracy of EEG in developing such system is also compared with other reference signals e.g., Electrocardiography (ECG), Finger temperature, Skin conductance, and Respiration. The results show that in decision making the system can handle individual variations and provides decision in each minute time interval.

  • 87.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning2014In: Sensors (Switzerland), ISSN 1424-8220, Vol. 14, no 7, p. 11770-11785Article in journal (Refereed)
    Abstract [en]

    Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems. 

  • 88.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Fusion Based System for Physiological Sensor Signal Classification2014In: Medicinteknikdagarna 2014 MTD10, 2014Conference paper (Refereed)
    Abstract [en]

    Today, usage of physiological sensor signals is essential in medical applications for diagnoses and classification of diseases. Clinicians often rely on information collected from several physiological sensor signals to diagnose a patient. However, sensor signals are mostly non-stationary and noisy, and single sensor signal could easily be contaminated by uncertain noises and interferences that could cause miscalculation of measurements and reduce clinical usefulness. Therefore, an apparent choice is to use multiple sensor signals that could provide more robust and reliable decision. Therefore, a physiological signal classification approach is presented based on sensor signal fusion and case-based reasoning. To classify Stressed and Relaxed individuals from physiological signals, data level and decision level fusion are performed and case-based reasoning is applied as classification algorithm. Five physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, data level fusion is performed using Multivariate Multiscale Entropy (MMSE) and extracted features are then used to build a case- library. Decision level fusion is performed on the features extracted using traditional time and frequency domain analysis. Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

  • 89.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Filla, Reno
    Volvo Construction Equipment, Sweden.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Örebro University, Sweden.
    Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning2014In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 41, no 2, p. 295-305Article in journal (Refereed)
    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.

  • 90.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Islam, Mohd. Siblee
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    K-NN Based Interpolation to Handle Artifacts for Heart Rate Variability Analysis2011In: IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011, IEEE , 2011, p. 387-392Conference paper (Refereed)
    Abstract [en]

    Heart rate variability (HRV) is a popular parameter for depicting activities of autonomous nervous system and helps to explain various physiological activities of the body. A small amount of artifacts can produce significant changes especially, for time domain HRV features. Manual correction of artifacts performed by visual inspection of the signal by experts is tedious and time consuming and often leads to incorrect result especially for long term recordings. Therefore, an automatic artifact removing approach that helps to provide clinically useful HRV analysis is valuable. This paper proposes an algorithm that detects and replaces artifacts from inter-beat interval (IBI) signal for HRV analysis. The detection is mainly based on windowing technique and interpolation is performed using the k-nearest neighbour (K-NN) algorithm. The experimental work shows a promising performance in handling artifacts for HRV analysis using electrocardiogram (ECG) sensor signal.

  • 91. Blobel, Bernd
    et al.
    Lindén, MariaMälardalen University, School of Innovation, Design and Engineering, Embedded Systems.Ahmed, Mobyen UddinMälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health: pHealth20152015Conference proceedings (editor) (Other academic)
  • 92.
    Islam, Mir Riyanul
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Flumeri, Gianluca Di
    Cognitive States in Operative Environment, BrainSigns, Via Sesto Celere, 7/C Rome, Italy.
    Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification2019In: Communications in Computer and Information Science, Volume 1107, 2019, p. 121-135Conference 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.

  • 93.
    Islam, Mir Riyanul
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hypothyroid Disease Diagnosis with Causal Explanation using Case-based Reasoning and Domain-specific Ontology2019In: Workshop on CBR in the Health Science WS-HealthCBR, 2019Conference 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.

  • 94.
    Köckemann, U.
    et al.
    Örebro University, Örebro, 70182, Sweden.
    Alirezaie, M.
    Örebro University, Örebro, 70182, Sweden.
    Renoux, J.
    Örebro University, Örebro, 70182, Sweden.
    Tsiftes, N.
    RISE SICS, RISE Research Institutes of Sweden, Stockholm, Sweden.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Morberg, Daniel
    Mälardalen University.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Loutfi, A.
    Örebro University, Örebro, 70182, Sweden.
    Open-source data collection and data sets for activity recognition in smart homes2020In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 3, article id 879Article in journal (Refereed)
    Abstract [en]

    As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

  • 95.
    Najib, Muhammad Sharfi
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Taib, M.N.
    Universiti Teknologi MARA, 40450, Selangor, Malaysia.
    Ali, N.A.M.
    Forest Research Institute Malaysia.
    Agarwood classification: A Case-Based Reasoning approach based on E-nose2012In: Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012, 2012, p. 120-126Conference paper (Refereed)
    Abstract [en]

    Using an array of sensors (E-nose) to classify Agarwood has proven to be successful and produced performance close to an expert level (90% of expert level performance) but it has proven difficult to eliminate misclassifications without over-fitting. In our effort to improve our result we explored a self-improving Case-Based Reasoning approach and reached 100% correct classification. Case-Based Reasoning is an approach that will learn from every new classified case and hence the risk for misclassification is reduced. Also when new cases have to be classified that have never occurred before the system will avoid misclassification (similarity measurement is low). The approach also enables indeterminism; in reality a sample may be both close to a good case and a bad case and need further exploration by experts. The approach also handles natural variants in the wood samples well; both low-quality and high-quality samples may spread considerably in the context of E-nose readings and there is no model available of low or high quality.

  • 96.
    Nilsson, Emma
    et al.
    Volvo Car Corporation, Sweden.
    Ahlström, Christer
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Fors, Carina
    VTI, Sweden.
    Lindén, Per
    Volvo Car Corporation, Sweden.
    Svanberg, Bo
    Volvo Car Corporation, Sweden.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Anund, Anna
    VTI, Sweden.
    Vehicle Driver Monitoring: sleepiness and cognitive load2017Report (Other academic)
    Abstract [en]

    To prevent road crashes, it is important to understand driver related contributing factors. The overall aim of the Vehicle Driver Monitoring project was to advance the understanding of two such factors; sleepiness and cognitive distraction. The project aimed at finding methods to measure the two states, with focus on physiological measures, and to study their effect on driver behaviour. The data collection was done in several laboratory and driving simulator experiments. Much new knowledge and insights were gained in the project. Significant effects of cognitive load as well as of sleepiness were found in several physiological measures. The results also showed that context, including individual and environmental factors, has a great impact on driver behaviours, measures and driver experiences.

  • 97.
    Olsson, Erik
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Ahmed, Mobyen Uddin
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    A CASE STUDY OF COMMUNICATION IN A DISTRIBUTED MULTI-AGENT SYSTEM IN A FACTORY PRODUCTION ENVIRONMENT2007Conference paper (Refereed)
    Abstract [en]

    A Distributed Multi-Agent System representing the behaviour of a machine maintenance procedure in a factory production environment is modelled using the BRIC language. The model provides an overview and simplification of the communication in the maintenance procedure. The model involves two distributed factory environments, each equipped with a Maintenance Agent and an Experience Sharing Agent. Maintenance agents can be seen as experts in interpreting local sensor data from the machine being observed. They have some basic domain knowledge about when to bring the findings to the attention of an agent, human or system. An agent is also autonomous and may have the trust to shut down a process. The maintenance agent will ask other agents or humans for assistance if bringing the macine ito working order is beyond the agent's ability. Necessary information about what maintenance actions to perform is provided by an Experience Sharing Agent which has the ability to identify past experience relevant for the current situation and thus beeing able to help the human to make a better and more informed decision avoiding previously, sometimes very costly mistakes.

  • 98.
    Olsson, Erik
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Hedelind, Mikael
    Mälardalen University, Department of Innovation, Design and Product Development.
    Ahmed, Mobyen Uddin
    Mälardalen University, Department of Computer Science and Electronics.
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    EXPERIENCE REUSE BETWEEN MOBILE PRODUCTION MODULES - AN ENABLER FOR THE FACTORY-IN-A-BOX CONCEPT2007Conference paper (Refereed)
    Abstract [en]

    Today's increased demand for flexible and fast reconfiguration of production systems is seen as one of the key factors for survival by many branches, especially small and medium sized enterprises. To enable adaptable and flexible production, we propose an integrated experience reuse system assisting in setup, operation, maintenance and repair. We present three subsystems that facilitate experience reuse between different engineers and operators working with standardised production modules. It is composed of three separate software components enabling: a) easy programming and control of robot cells, b) monitoring and condition based maintenance, c) distributed experience reuse. The results presented in this paper have been developed within the Factory-in-a-Box project, the ExAct project and the Eken project.

  • 99.
    Prytz, Erik
    et al.
    Linköpings Universitet.
    Kristoffersson, Annica
    Örebro Universitet.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Petrovic, Nikola
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Lind, Leili
    Linköpings Universitet.
    E-care@home: Initial Usability Considerations2016In: Abstractproceedings from Medicinteknikdagarna 2016, Örebro, Sweden, 2016Conference paper (Refereed)
    Abstract [en]

    The E-care@home project aims to develop a smart sensor and communication infrastructure with semantic interoperability capable of monitoring patients in their home. The system will use a wide array of sensors, both medical and environmental, and an advanced computer reasoning layer capable of interpreting sensor data and deliver natural language summaries of the patient health status. One critical issue is that of the usability of such a system to the users, i.e. patients and care providers.

  • 100.
    Rahman, Hamidur
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Non-contact-based driver's cognitive load classification using physiological and vehicular parameters2020In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 55, article id 101634Article in journal (Refereed)
    Abstract [en]

    Classification of cognitive load for vehicular drivers is a complex task due to underlying challenges of the dynamic driving environment. Many previous works have shown that physiological sensor signals or vehicular data could be a reliable source to quantify cognitive load. However, in driving situations, one of the biggest challenges is to use a sensor source that can provide accurate information without interrupting diverging tasks. In this paper, instead of traditional wire-based sensors, non-contact camera and vehicle data are used that have no physical contact with the driver and do not interrupt driving. Here, four machine learning algorithms, logistic regression (LR), support vector machine (SVM), linear discriminant analysis (LDA) and neural networks (NN), are investigated to classify the cognitive load using the collected data from a driving simulator study. In this paper, physiological parameters are extracted from facial video images, and vehicular parameters are collected from controller area networks (CAN). The data collection was performed in close collaboration with industrial partners in two separate studies, in which study-1 was designed with a 1-back task and study-2 was designed with both 1-back and 2-back task. The goal of the experiment is to investigate how accurately the machine learning algorithms can classify drivers' cognitive load based on the extracted features in complex dynamic driving environments. According to the results, for the physiological parameters extracted from the facial videos, the LR model with logistic function outperforms the other three classification methods. Here, in study-1, the achieved average accuracy for the LR classifier is 94% and in study-2 the average accuracy is 82%. In addition, the classification accuracy for the collected physiological parameters was compared with reference wire-sensor signals. It is observed that the classification accuracies between the sensor and the camera are very similar; however, better accuracy is achieved with the camera data due to having lower artefacts than the sensor data. 

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