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Ahmed, M. U., Begum, S., Catalina, C. A., Limonad, L., Hök, B. & Flumeri, G. D. (2018). Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 101-106).
Open this publication in new window or tab >>Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport
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2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 101-106Conference 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.

Keywords
SimuSafe, safer transport, data-analysis, big data, human factor
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37085 (URN)10.1007/978-3-319-76213-5_14 (DOI)2-s2.0-85042536073 (Scopus ID)9783319762128 (ISBN)
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: 2018-03-08Bibliographically approved
Rahman, H., Ahmed, M. U. & Begum, S. (2018). Deep Learning based Person Identification using Facial Images. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 111-115).
Open this publication in new window or tab >>Deep Learning based Person Identification using Facial Images
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 111-115Conference 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.

Keywords
Face recognition, Person Identification, Deep Learning.
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37091 (URN)10.1007/978-3-319-76213-5_17 (DOI)2-s2.0-85042545019 (Scopus ID)9783319762128 (ISBN)
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: 2018-03-08Bibliographically approved
Barua, S., Ahmed, M. U. & Begum, S. (2018). Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 26-33).
Open this publication in new window or tab >>Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 26-33Conference 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.

Keywords
Physiological signals, distributed analytics, case-based reasoning
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37076 (URN)10.1007/978-3-319-76213-5_4 (DOI)2-s2.0-85042522774 (Scopus ID)9783319762128 (ISBN)
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: 2018-03-08Bibliographically approved
Ahmed, M. U., Rahman, H. & Begum, S. (2018). Quality index analysis on camera- A sed R-eak identification considering movements and light illumination. In: Studies in Health Technology and Informatics, vol 249: . Paper presented at 15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018 (pp. 84-92). IOS Press
Open this publication in new window or tab >>Quality index analysis on camera- A sed R-eak identification considering movements and light illumination
2018 (English)In: Studies in Health Technology and Informatics, vol 249, IOS Press , 2018, p. 84-92Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: Normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ±200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men. 

Place, publisher, year, edition, pages
IOS Press, 2018
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-40196 (URN)10.3233/978-1-61499-868-6-84 (DOI)2-s2.0-85049018248 (Scopus ID)9781614998679 (ISBN)
Conference
15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018
Available from: 2018-07-05 Created: 2018-07-05 Last updated: 2018-07-05Bibliographically approved
Ahmed, M. U., Rahman, H. & Begum, S. (2018). Quality Index Analysis on Camera-based R-peak Identification Considering Movements and Light Illumination. In: 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018: . Paper presented at 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018, 12 Jun 2018, Gjövik, Norway.
Open this publication in new window or tab >>Quality Index Analysis on Camera-based R-peak Identification Considering Movements and Light Illumination
2018 (English)In: 15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018, 2018Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ?200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men.

Keywords
Quality Index Analysis, Camera-based system, R-peak Identification
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-39258 (URN)
Conference
15th International Conference on Wearable, Micro & Nano technologies for Personalized Health pHealth2018, 12 Jun 2018, Gjövik, Norway
Projects
HR R-peak detection quality index analysis
Available from: 2018-05-23 Created: 2018-05-23 Last updated: 2018-05-23Bibliographically approved
Barua, S., Begum, S. & Ahmed, M. U. (2018). Scalable Framework for Distributed Case-based Reasoning for Big data analytics. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 111-114).
Open this publication in new window or tab >>Scalable Framework for Distributed Case-based Reasoning for Big data analytics
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 111-114Conference 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.

Keywords
Distributed analytics, case-based reasoning
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37087 (URN)10.1007/978-3-319-76213-5_16 (DOI)2-s2.0-85042527811 (Scopus ID)9783319762128 (ISBN)
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: 2018-03-08Bibliographically approved
Rahman, H., Ahmed, M. U. & Begum, S. (2018). Vision-Based Remote Heart Rate Variability Monitoring using Camera. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 10-18).
Open this publication in new window or tab >>Vision-Based Remote Heart Rate Variability Monitoring using Camera
2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 10-18Conference paper, Published paper (Refereed)
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.

Keywords
Physiological signals, Heart Rate, Inter-beat-Interval, Heart-Rate-Variability, Non-contact, Remote Monitoring.
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37072 (URN)10.1007/978-3-319-76213-5_2 (DOI)2-s2.0-85042538568 (Scopus ID)9783319762128 (ISBN)
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: 2017-10-31 Created: 2017-10-31 Last updated: 2018-03-08Bibliographically approved
Barua, S., Ahmed, M. U., Ahlström, C., Begum, S. & Funk, P. (2017). Automated EEG Artifact Handling with Application in Driver Monitoring. IEEE journal of biomedical and health informatics, PP(99)
Open this publication in new window or tab >>Automated EEG Artifact Handling with Application in Driver Monitoring
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2017 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. PP, no 99Article in journal (Refereed) Epub ahead of print
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.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Artifacts, Clustering, Electroencephalogram, Independent Component Analysis, Wavelet decomposition
National Category
Signal Processing
Identifiers
urn:nbn:se:mdh:diva-37347 (URN)10.1109/JBHI.2017.2773999 (DOI)2-s2.0-85035807991 (Scopus ID)
Projects
VDM - Vehicle Driver MonitoringSafeDriver: A Real Time Driver's State Monitoring and Prediction System
Available from: 2017-11-27 Created: 2017-11-27 Last updated: 2018-03-28Bibliographically approved
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, p. 99-106Article 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. 

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)000426824800011 ()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: 2018-03-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1212-7637

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