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Ahmed, M. U. & Begum, S. (2017). Big Data Analytics in Health Monitoring at Home. In: Medicinteknikdagarna 2017 MTD 2017: . Paper presented at Medicinteknikdagarna 2017 MTD 2017, 09 Oct 2017, Västerås, Sweden. .
Open this publication in new window or tab >>Big Data Analytics in Health Monitoring at Home
2017 (English)In: Medicinteknikdagarna 2017 MTD 2017, 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposed a big data analytics approach applied in the projects ESS-H and E-care@home in the context of biomedical and health informatics with the advancement of information fusion, data abstraction, data mining, knowledge discovery, learning, and reasoning [1][2]. Data are collected through the projects, considering both the health parameters, e.g. temperature, bio-impedance, skin conductance, heart sound, blood pressure, pulse, respiration, weight, BMI, BFP, movement, activity, oxygen saturation, blood glucose, heart rate, medication compliance, ECG, EMG, and EEG, and the environmental parameters e.g. force/pressure, infrared (IR), light/luminosity, photoelectric, room-temperature, room-humidity, electrical usage, water usage, RFID localization and accelerometers. They are collected as semi-structured/unstructured, continuous/periodic, digital/paper record, single/multiple patients, once/several-times, etc. and stored in a central could server [5]. Thus, with the help of embedded system, digital technologies, wireless communication, Internet of Things (IoT) and smart sensors, massive quantities of data (so called ‘Big Data’) with value, volume, velocity, variety, veracity and variability are achieved [2]. The data analysis work in the following three steps. In Step1, pre-processing, future extraction and selection are performed based on a combination of statistical, machine learning and signal processing techniques. A novel strategy to fuse the data at feature level and as well as at data level considers a defined fusion mechanism [3]. In Step2, a combination of potential sequences in the learning and search procedure is investigated. Data mining and knowledge discovery, using the refined data from the above for rule extraction and knowledge mining, with support for anomaly detection, pattern recognition and regression are also explored here [4]. In Step3, adaptation of knowledge representation approaches is achieved by combining different artificial intelligence methods [3] [4]. To provide decision support a hybrid approach is applied utilizing different machine learning algorithms, e.g. case-based reasoning, and clustering [4]. The approach offers several data analytics tasks, e.g. information fusion, anomaly detection, rules and knowledge extraction, clustering, pattern identification, correlation analysis, linear regression, logic regression, decision trees, etc. Thus, the approach assist in decision support, early detection of symptoms, context awareness and patient’s health status in a personal environment.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37029 (URN)
Conference
Medicinteknikdagarna 2017 MTD 2017, 09 Oct 2017, Västerås, Sweden
Projects
ESS-H - Embedded Sensor Systems for Health Research Profileecare@home
Available from: 2017-11-20 Created: 2017-11-20 Last updated: 2017-11-20Bibliographically approved
Barua, S., Ahmed, M. U. & Begum, S. (2017). Classifying drivers' cognitive load using EEG signals. Paper presented at 14th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2017; Eindhoven; Netherlands; 14 May 2017 through 16 May 2017. Studies in Health Technology and Informatics, 237, 99-106.
Open this publication in new window or tab >>Classifying drivers' cognitive load using EEG signals
2017 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 237, 99-106 p.Article in journal (Refereed) Published
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. © 2017 The authors and IOS Press.

Place, publisher, year, edition, pages
IOS Press, 2017
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-35636 (URN)10.3233/978-1-61499-761-0-99 (DOI)2-s2.0-85019484755 (Scopus ID)9781614997603 (ISBN)
Conference
14th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2017; Eindhoven; Netherlands; 14 May 2017 through 16 May 2017
Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2017-09-27Bibliographically approved
Ahmed, M. U., Begum, S., Catalina, C. A., Limonad, L., Hök, B. & Flumeri, G. D. (2017). Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport. In: 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France. .
Open this publication in new window or tab >>Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport
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2017 (English)In: 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Improving safer transport includes individual and collective behavioural aspects and their interaction. A system that can monitor and evaluate the human cognitive and physical capacities based on human factor measurement is often beneficial to improve safety in driving condition. However, analysis and evaluation of human factor measurement i.e. Demographics, Behavioural and Physiological in real-time is challenging. This paper presents a methodology for cloud-based data analysis, categorization and metrics correlation in real-time through a H2020 project called SimuSafe. Initial implementation of this methodology shows a step-by-step approach which can handle huge amount of data with variation and verity in the cloud.

Keyword
SimuSafe, safer transport, data-analysis, big data, human factor
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37085 (URN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
SimuSafe : Simulator of Behavioural Aspects for Safer Transport
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2017-10-27Bibliographically approved
Rahman, H., Ahmed, M. U. & Begum, S. (2017). Deep Learning based Person Identification using Facial Images. In: 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France. .
Open this publication in new window or tab >>Deep Learning based Person Identification using Facial Images
2017 (English)In: 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 2017Conference paper, Published paper (Refereed)
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.

Keyword
Face recognition, Person Identification, Deep Learning.
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37091 (URN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
SafeDriver: A Real Time Driver's State Monitoring and Prediction System
Available from: 2017-10-26 Created: 2017-10-26 Last updated: 2017-10-26Bibliographically approved
Barua, S., Ahmed, M. U. & Begum, S. (2017). Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring. In: 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France. .
Open this publication in new window or tab >>Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring
2017 (English)In: 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 2017Conference paper, Published 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.

Keyword
Physiological signals, distributed analytics, case-based reasoning
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37076 (URN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
VDM - Vehicle Driver Monitoring
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2017-10-31Bibliographically approved
Begum, S., Kerstis, B., Barua, S., Westerlund, H. & Hjortsberg, C. (2017). Food4You: A Personalized System for Adaptive Mealtime Situations for Elderly. In: Medicinteknikdagarna 2017 MTD 2017: . Paper presented at Medicinteknikdagarna 2017 MTD 2017, 09 Oct 2017, Västerås, Sweden. .
Open this publication in new window or tab >>Food4You: A Personalized System for Adaptive Mealtime Situations for Elderly
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2017 (English)In: Medicinteknikdagarna 2017 MTD 2017, 2017Conference paper, Poster (with or without abstract) (Refereed)
Keyword
Elderly, Mealtime situation, Data Mining, Knowledge base
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37030 (URN)
Conference
Medicinteknikdagarna 2017 MTD 2017, 09 Oct 2017, Västerås, Sweden
Projects
Food4Health: A Personalized System for Adaptive Mealtime Situations for Elderly
Available from: 2017-11-20 Created: 2017-11-20 Last updated: 2017-11-20Bibliographically approved
Barua, S., Begum, S. & Ahmed, M. U. (2017). Intelligent automated eeg artifacts handling using wavelet transform, independent component analysis and hierarchal clustering. In: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng.: . Paper presented at 14 November 2016 through 16 November 2016 (pp. 144-148). Springer Verlag.
Open this publication in new window or tab >>Intelligent automated eeg artifacts handling using wavelet transform, independent component analysis and hierarchal clustering
2017 (English)In: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng., Springer Verlag , 2017, 144-148 p.Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer Verlag, 2017
Keyword
Electroencephalogram (EEG), Hierarchical clustering, Muscle artifacts, Ocular artifacts, Brain, Health care, Independent component analysis, Mobile telecommunication systems, Muscle, Wavelet transforms, Electrical signal, Electro-encephalogram (EEG), Hier-archical clustering, Independent component analysis(ICA), Physiological signals, Recorded signals, Visual inspection, Electroencephalography
National Category
Computer and Information Science
Identifiers
urn:nbn:se:mdh:diva-36063 (URN)10.1007/978-3-319-58877-3_19 (DOI)2-s2.0-85020877843 (Scopus ID)9783319588766 (ISBN)
Conference
14 November 2016 through 16 November 2016
Available from: 2017-07-06 Created: 2017-07-06 Last updated: 2017-07-06Bibliographically approved
Begum, S., Barua, S. & Ahmed, M. U. (2017). In-Vehicle Stress Monitoring Based on EEG Signal. International Journal of Engineering Research and Applications, 7(7), 55-71.
Open this publication in new window or tab >>In-Vehicle Stress Monitoring Based on EEG Signal
2017 (English)In: International Journal of Engineering Research and Applications, ISSN 2248-9622, E-ISSN 2248-9622, Vol. 7, no 7, 55-71 p.Article in journal (Refereed) Published
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.

Keyword
Keywords: Stress, Monitoring System, Electroencephalography (EEG), Case-Based Reasoning (CBR), Largest Lyapunov Exponent (LLE)
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37035 (URN)10.9790/9622-0707095571 (DOI)
Projects
SafeDriver: A Real Time Driver's State Monitoring and Prediction System
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2017-11-29Bibliographically approved
Barua, S., Begum, S. & Ahmed, M. U. (2017). Scalable Framework for Distributed Case-based Reasoning for Big data analytics. In: 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France. .
Open this publication in new window or tab >>Scalable Framework for Distributed Case-based Reasoning for Big data analytics
2017 (English)In: 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 2017Conference paper, Published 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.

Keyword
Distributed analytics, case-based reasoning
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37087 (URN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
VDM - Vehicle Driver Monitoring
Available from: 2017-10-27 Created: 2017-10-27 Last updated: 2017-10-27Bibliographically approved
Nilsson, E., Ahlström, C., Barua, S., Fors, C., Lindén, P., Svanberg, B., . . . Anund, A. (2017). Vehicle Driver Monitoring: sleepiness and cognitive load. Linköping, Sweden: VTI.
Open this publication in new window or tab >>Vehicle Driver Monitoring: sleepiness and cognitive load
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2017 (English)Report (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.

Place, publisher, year, edition, pages
Linköping, Sweden: VTI, 2017
Series
VTI rapport
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:mdh:diva-37031 (URN)
Projects
VDM - Vehicle Driver Monitoring
Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2017-11-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1212-7637

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