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  • 51.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Folke, Mia
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments2011Ingår i: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews, ISSN 1094-6977, E-ISSN 1558-2442, Vol. 41, nr 4, s. 421-434Artikel i tidskrift (Refereegranskat)
    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.

  • 52.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Folke, Mia
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    A computer-based system for the assessment and diagnosis of individual sensitivity to stress in Psychophysiology2007Konferensbidrag (Refereegranskat)
    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.

  • 53.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Schéele, Bo von
    Mälardalens högskola, Akademin för innovation, design och teknik.
    A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching2009Ingår i: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 25, nr 3, s. 180-195Artikel i tidskrift (Refereegranskat)
    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.

  • 54.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning2007Ingår i: 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, s. 478-491Kapitel i bok, del av antologi (Refereegranskat)
    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.

  • 55.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Individualized Stress Diagnosis Using Calibration and Case-Based Reasoning2007Ingår i: Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society, Borås, Sweden, 2007, s. 59-69Konferensbidrag (Refereegranskat)
    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.

  • 56.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Similarity of Medical Cases in Health Care Using Cosine Similarity and Ontology2007Konferensbidrag (Refereegranskat)
    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.

  • 57.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Xiong, Ning
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    von Schéele, Bo
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress2006Ingår i: 8th European Workshop on Case-based Reasoning in the Health Sciences, workshop proceedings, 2006, s. 113-122Konferensbidrag (Refereegranskat)
    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.

  • 58.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Folke, Mia
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Diagnosis and Biofeedback System for Stress2009Ingår i: Proceedings of the 6th International Workshop on Wearable, Micro, and Nano Technologies for Personalized Health: "Facing Future Healthcare Needs", pHealth 2009, 2009, s. 17-20Konferensbidrag (Refereegranskat)
    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.

  • 59.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    von Schéele, Bo
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Erik
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Development of a Stress Questionnaire: A Tool for Diagnosing Mental Stress2010Rapport (Övrigt vetenskapligt)
    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.

  • 60.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management2010Ingår i: Computational Intelligence in Healthcare 4: Advanced Methodologies / [ed] Isabelle Bichindaritz et. al., Springer Berlin/Heidelberg, 2010, s. 159-189Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    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.

  • 61.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik.
    EEG Sensor Based Classification for Assessing Psychological Stress2013Ingår i: Studies in Health Technology and Informatics, Volume 189, 2013, IOS Press, 2013, s. 83-88Konferensbidrag (Refereegranskat)
    Abstract [en]

    Electroencephalogram (EEG) reflects the brain activity and is widely used in biomedical research. However, analysis of this signal is still a challenging issue. This paper presents a hybrid approach for assessing stress using the EEG signal. It applies Multivariate Multi-scale Entropy Analysis (MMSE) for the data level fusion. Case-based reasoning is used for the classification tasks. Our preliminary result indicates that EEG sensor based classification could be an efficient technique for evaluation of the psychological state of individuals. Thus, the system can be used for personal health monitoring in order to improve users health.

  • 62.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    In-Vehicle Stress Monitoring Based on EEG Signal2017Ingår i: International Journal of Engineering Research and Applications, ISSN 2248-9622, E-ISSN 2248-9622, Vol. 7, nr 7, s. 55-71Artikel i tidskrift (Refereegranskat)
    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.

  • 63.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning2014Ingår i: Sensors (Switzerland), ISSN 1424-8220, Vol. 14, nr 7, s. 11770-11785Artikel i tidskrift (Refereegranskat)
    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. 

  • 64.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Fusion Based System for Physiological Sensor Signal Classification2014Ingår i: Medicinteknikdagarna 2014 MTD10, 2014Konferensbidrag (Refereegranskat)
    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.

  • 65.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Filla, Reno
    Volvo Construction Equipment, Sweden.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Örebro University, Sweden.
    Classification of physiological signals for wheel loader operators using Multi-scale Entropy analysis and case-based reasoning2014Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 41, nr 2, s. 295-305Artikel i tidskrift (Refereegranskat)
    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.

  • 66.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Behnam, Moris
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Larsson, Thomas B
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Nolte, Thomas
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sandström, Kristian
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Towards a Compositional Service Architecture for Real-Time Cloud Robotics2016Ingår i: ACM SIGBED Review, E-ISSN 1551-3688, s. 63-64Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper we present our ongoing work towards a compositional service architecture that integrates cloud technology for computational capacity targeting real-time robotics applications. In particular we take a look at the challenges inherent within the data center where the services are executing. We outline characteristics of the services used in the real-time cloud robotics application, along with the service management and corresponding task model used to execute services. We identify several key central challenges that must be addressed towards integrating cloud technology in real-time robotics.

  • 67.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Islam, Mohd. Siblee
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    K-NN Based Interpolation to Handle Artifacts for Heart Rate Variability Analysis2011Ingår i: IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011, IEEE , 2011, s. 387-392Konferensbidrag (Refereegranskat)
    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.

  • 68.
    Begum, Shahina
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Kerstis, Birgitta
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Westerlund, Hanna
    Camanio Care AB, Sweden.
    Hjortsberg, Cecilia
    Västerås stad, Sweden.
    Food4You: A Personalized System for Adaptive Mealtime Situations for Elderly2017Ingår i: Medicinteknikdagarna 2017 MTD 2017, 2017Konferensbidrag (Refereegranskat)
  • 69.
    Begum, Shahina
    et al.
    Mälardalens högskola, Institutionen för datavetenskap och elektronik. Dalarna University, Borlänge, Sweden .
    Westin, Jerker
    Dalarna University, Borlänge, Sweden .
    Funk, Peter
    Mälardalens högskola, Institutionen för datavetenskap och elektronik.
    Dougherty, Mark
    Dalarna University, Borlänge, Sweden .
    Induction of an Adaptive Neuro-Fuzzy Inference System for Investigating Fluctuation in Parkinson´s Disease: The 23rd Annual Workshop of the Swedish Artificial Intelligence Society Umeå, May 10-12, 20062006Ingår i: Proceedings of SAIS 2006, 2006, s. 67-72Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge, supported by prior knowledge and statistical modeling. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to "discover" relationships between fluctuation, treatment and disease severity in Parkinson. Preliminary results from this project are promising and show that Neuro-fuzzy techniques in combination with statistical methods may offer medical research and medical applications a useful combination of methods.

  • 70.
    Islam, Mir Riyanul
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    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 Classification2019Ingår i: The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 2019Konferensbidrag (Refereegranskat)
    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.

  • 71.
    Islam, Mir Riyanul
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Hypothyroid Disease Diagnosis with Causal Explanation using Case-based Reasoning and Domain-specific Ontology2019Ingår i: Workshop on CBR in the Health Science WS-HealthCBR, 2019Konferensbidrag (Refereegranskat)
    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.

  • 72.
    Nilsson, Emma
    et al.
    Volvo Car Corporation, Sweden.
    Ahlström, Christer
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Fors, Carina
    VTI, Sweden.
    Lindén, Per
    Volvo Car Corporation, Sweden.
    Svanberg, Bo
    Volvo Car Corporation, Sweden.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Anund, Anna
    VTI, Sweden.
    Vehicle Driver Monitoring: sleepiness and cognitive load2017Rapport (Övrigt vetenskapligt)
    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.

  • 73.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Deep Learning based Person Identification using Facial Images2018Ingår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, s. 111-115Konferensbidrag (Refereegranskat)
    Abstract [en]

    Person identification is an important task for many applications for example in security. A person can be identified using finger print, vocal sound, facial image or even by DNA test. However, Person identification using facial images is one of the most popular technique which is non-contact and easy to implement and a research hotspot in the field of pattern recognition and machine vision. n this paper, a deep learning based Person identification system is proposed using facial images which shows higher accuracy than another traditional machine learning, i.e. Support Vector Machine.

  • 74.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Non-contact heart rate monitoring using lab color space2016Ingår i: Studies in Health Technology and Informatics, 2016, Vol. 224, s. 46-53Konferensbidrag (Refereegranskat)
    Abstract [en]

    Research progressing during the last decade focuses more on non-contact based systems to monitor Heart Rate (HR) which are simple, low-cost and comfortable to use. Most of the non-contact based systems are using RGB videos which is suitable for lab environment. However, it needs to progress considerably before they can be applied in real life applications. As luminance (light) has significance contribution on RGB videos HR monitoring using RGB videos are not efficient enough in real life applications in outdoor environment. This paper presents a HR monitoring method using Lab color facial video captured by a webcam of a laptop computer. Lab color space is device independent and HR can be extracted through facial skin color variation caused by blood circulation considering variable environmental light. Here, three different signal processing methods i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) have been applied on the color channels in video recordings and blood volume pulse (BVP) has been extracted from the facial regions. In this study, HR is subsequently quantified and compare with a reference measurement. The result shows that high degrees of accuracy have been achieved compared to the reference measurements. Thus, this technology has significant potential for advancing personal health care, telemedicine and many real life applications such as driver monitoring.

  • 75.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Non-Contact Physiological Parameters Extraction Using Camera2016Ingår i: Internet of Things. IoT Infrastructures: Second International Summit, IoT 360° 2015 Rome, Italy, October 27–29, 2015. Revised Selected Papers, Part I, 2016, Vol. 169, s. 448-453Konferensbidrag (Refereegranskat)
    Abstract [en]

    Physiological parameters such as Heart Rate (HR), Beat-to-Beat Interval (IBI) and Respiration Rate (RR) are vital indicators of people’s physiological state and important to monitor. However, most of the measurements methods are connection based, i.e. sensors are connected to the body which is often complicated and requires personal assistance. This paper proposed a simple, low-cost and non-contact approach for measuring multiple physiological parameters using a web camera in real time. Here, the heart rate and respiration rate are obtained through facial skin colour variation caused by body blood circulation. Three different signal processing methods such as Fast Fourier Transform (FFT), independent component analysis (ICA) and Principal component analysis (PCA) have been applied on the colour channels in video recordings and the blood volume pulse (BVP) is extracted from the facial regions. HR, IBI and RR are subsequently quantified and compared to corresponding reference measurements. High degrees of agreement are achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine. 

  • 76.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Vision-Based Remote Heart Rate Variability Monitoring using Camera2018Ingår i: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, s. 10-18Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 77.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Real Time Heart Rate Monitoring from Facial RGB Color Video using Webcam2016Ingår i: The 29th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2016, Malmö, Sweden, 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Heart Rate (HR) is one of the most important Physiological parameter and a vital indicator of people’s physiological state and is therefore important to monitor. Monitoring of HR often involves high costs and complex application of sensors and sensor systems. Research progressing during last decade focuses more on noncontact based systems which are simple, low-cost and comfortable to use. Still most of the noncontact based systems are fit for lab environments in offline situation but needs to progress considerably before they can be applied in real time applications. This paper presents a real time HR monitoring method using a webcam of a laptop computer. The heart rate is obtained through facial skin color variation caused by blood circulation. Three different signal processing methods such as Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) have been applied on the color channels in video recordings and the blood volume pulse (BVP) is extracted from the facial regions. HR is subsequently quantified and compared to corresponding reference measurements. The obtained results show that there is a high degrees of agreement between the proposed experiments and reference measurements. This technology has significant potential for advancing personal health care and telemedicine. Further improvements of the proposed algorithm considering environmental illumination and movement can be very useful in many real time applications such as driver monitoring.

  • 78.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Hök, Bertil
    Hök Instrument AB.
    A Case-Based Classification for Drivers’ Alcohol Detection Using Physiological Signals2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a case-based classification system for alcohol detection using physiological parameters. Here, four physiological parameters e.g. Heart Rate Variability (HRV), Respiration Rate (RR), Finger Temperature (FT), and Skin Conductance (SC) are used in a Case-based reasoning (CBR) system to detect alcoholic state. In this study, the participants are classified into two groups as drunk or sober. The experimental work shows that using the CBR classification approach the obtained accuracy for individual physiological parameters e.g., HRV is 85%, RR is 81%, FT is 95% and SC is 86%. On the other hand, the achieved accuracy is 88% while combining the four parameters i.e., HRV, RR, FT and SC using the CBR system. So, the evaluation illustrates that the CBR system based on physiological sensor signal can classify alcohol state accurately when a person is under influence of at least 0.2 g/l of alcohol.

  • 79.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Intelligent Driver Monitoring Based on Physiological Sensor Signals: Application Using Camera2015Ingår i: IEEE 18th International Conference on Intelligent Transportation Systems ITSC2015, Canary Islands, Spain, 2015, s. 2637-2642Konferensbidrag (Refereegranskat)
    Abstract [en]

    Recently, there has been increasing interest in low-cost, non-contact and pervasive methods for monitoring physiological information for the drivers. For the intelligent driver monitoring system there has been so many approaches like facial expression based method, driving behavior based method and physiological parameters based method. Physiological parameters such as, heart rate (HR), heart rate variability (HRV), respiration rate (RR) etc. are mainly used to monitor physical and mental state. Also, in recent decades, there has been increasing interest in low-cost, non-contact and pervasive methods for measuring physiological information. Monitoring physiological parameters based on camera images is such kind of expected methods that could offer a new paradigm for driver’s health monitoring. In this paper, we review the latest developments in using camera images for non-contact physiological parameters that provides a resource for researchers and developers working in the area.

  • 80.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Driver monitoring in the context of autonomous vehicle2015Ingår i: Frontiers in Artificial Intelligence and Applications, Amsterdam, 2015, Vol. 278, s. 108-117Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today research is going on within different essential functions need to bring automatic vehicles to the roads. However, there will be manual driven vehicles for many years before it is fully automated vehicles on roads. In complex situations, automated vehicles will need human assistance for long time. So, for road safety driver monitoring is even more important in the context of autonomous vehicle to keep the driver alert and awake. But, limited effort has been done in total integration between automatic vehicle and human driver. Therefore, human drivers need to be monitored and able to take over control within short notice. This papers provides an overview on autonomous vehicles and un-obstructive driver monitoring approaches that can be implemented in future autonomous vehicles to monitor driver e.g., to diagnose and predict stress, fatigue etc. in semi-automated vehicles. 

  • 81.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ins and Outs of Big Data: A Review2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today with the fast development of digital technologies and advance communications a gigantic amount of data sets with massive and complex structures called ‘Big data’ is being produced everyday enormously and exponentially. Again, the arrival of social media, advent of smart homes, offices and hospitals are connected as Internet of Things (IoT), this influence also a lot to Big data. According to the study, Big data presents data sets with large magnitude including structured, semi-structured or unstructured data. The study also presents the new technologies for data analyzing, collecting, fast searching, proper sharing, exact storing, speedy transferring, hidden pattern visualization and violations of privacy etc. This paper presents an overview of ins and outs of Big Data where the content, scope, samples, methods, advantages, challenges and privacy of Big data have been discussed. The goal of this article is to provide big data knowledge to the research community for the sake of its many real life applications such as traffic management, driver monitoring, health care in hospitals, meteorology and so on.

  • 82.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Iyer, Shankar
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Meusburger, Caroline
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Dobrovoljski, Kolja
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Stoycheva, Mihaela
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Turkulov, Vukan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    SmartMirror: An Embedded Non-contact System for Health Monitoring at Home2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    The ‘Smart Mirror’ project introduces non-contact based technological innovations at our homes where its usage can be as ubiquitous as ‘looking at a mirror’ while providing critical actionable insights thereby leading to improved care and outcomes. The key objectives is to detect key physiological markers like Heart Rate (HR), Respiration Rate (RR), Inter-beat-interval (IBI) and Blood Pressure (BP) and also drowsiness using the video input of the individual standing in front of the mirror and display the results in real-time. A satisfactory level of accuracy has been attained with respect to the reference sensors signal.

  • 83.
    Rahman, Hamidur
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Sandberg, Johan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Eriksson, Lennart
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Heidari, Mohammad
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Arwald, Jan
    Exformation AB, Lidingö, Sweden.
    Eriksson, Peter
    Exformation AB, Lidingö, Sweden.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Falling Angel - a Wrist Worn Fall Detection System Using K-NN Algorithm2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    A wrist worn fall detection system has been developed where the accelerometer data from an angel sensor is analyzed by a two-layered algorithm in an android phone. Here, the first layer uses a threshold to find potential falls and if the thresholds are met, then in the second layer a machine learning i.e., k-Nearest Neighbor (k-NN) algorithm analyses the data to differentiate it from Activities of Daily Living (ADL) in order to filter out false positives. The final result of this project using the k-NN algorithm provides a classification sensitivity of 96.4%. Here, the acquired sensitivity is 88.1% for the fall detection and the specificity for ADL is 98.1%.

  • 84.
    Sheuly, Sharmin Sultana
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Bankarusamy, Sudhangathan
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Behnam, Moris
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Resource allocation in industrial cloud computing using artificial intelligence algorithms2015Ingår i: Frontiers in Artificial Intelligence and Applications, Volume 278, 2015, s. 128-136Konferensbidrag (Refereegranskat)
    Abstract [en]

    Cloud computing has recently drawn much attention due to the benefits that it can provide in terms of high performance and parallel computing. However, many industrial applications require certain quality of services that need efficient resource management of the cloud infrastructure to be suitable for industrial applications. In this paper, we focus mainly on the services, usually executed within virtual machines, allocation problem in the cloud network. To meet the quality of service requirements we investigate different algorithms that can achieve load balancing which may require migrating virtual machines from one node/server to another during runtime and considering both CPU and communication resources. Three different allocation algorithms based on Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Best-fit heuristic algorithm are applied in this paper. We evaluate the three algorithms in terms of cost/objective function and calculation time. In addition, we explore how tuning different parameters (including population size, probability of mutation and probability of crossover) can affect the cost/objective function in GA. Depending on the evaluation, it is concluded that algorithm performance is dependent on the circumstances i.e. available resource, number of VMs etc. 

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