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  • 1.
    Ali Hamad, Rebeen
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Lundström, Jens
    JeCom Consulting, Halmstad, Sweden.
    Stability analysis of the t-SNE algorithm for human activity pattern data2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.

  • 2.
    Eriksson, Helena
    et al.
    Högskolan i Halmstad, Akademin för hälsa och välfärd, Centrum för forskning om välfärd, hälsa och idrott (CVHI), Wigforss-gruppen.
    Isaksson, Anna
    Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, Centrum för samhällsanalys (CESAM).
    Lundström, Jens
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Technology and trust: Social and technical innovation in elderly care2013Ingår i: Abstracts EGI2013, 2013, s. 17-18Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    A demographic change is occurring in many areas of the world. The elderly population share has been increasing for the last decades and estimations predict that this group will be large in proportion to the number of economically active younger people. This change will bring exponentially increasing costs of health care. Technical developments could be one way to meet these new challenges. In a recent study called “Safe at night” the aim was to investigate whether a technical solution based can be used to supplement the home care work, with focus on the nightly visits of the elderly. The study raises questions regarding technical issues as well as actors (users, relatives and staffs) perspective on the methods. Researchers from both social and technical disciplines were involved in the study. In this paper, we highlight the importance of scientists from different disciplines participating in the study, as well as municipalities and industry. We show in particular the knowledge gained from a technical perspective and from a social science perspective and how and why these perspectives together constitute the necessary components to create innovation regarding elderly care and issues related to technology and trust.

  • 3.
    Lundström, Jens
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Situation Awareness in Colour Printing and Beyond2014Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Machine learning methods are increasingly being used to solve real-world problems in the society. Often, the complexity of the methods are well hidden for users. However, integrating machine learning methods in real-world applications is not a straightforward process and requires knowledge both about the methods and domain knowledge of the problem. Two such domains are colour print quality assessment and anomaly detection in smart homes, which are currently driven by manual monitoring of complex situations. The goal of the presented work is to develop methods, algorithms and tools to facilitate monitoring and understanding of the complex situations which arise in colour print quality assessment and anomaly detection for smart homes. The proposed approach builds on the use and adaption of supervised and unsupervised machine learning methods.

    Novel algorithms for computing objective measures of print quality in production are proposed in this work. Objective measures are also modelled to study how paper and press parameters influence print quality. Moreover, a study on how print quality is perceived by humans is presented and experiments aiming to understand how subjective assessments of print quality relate to objective measurements are explained. The obtained results show that the objective measures reflect important aspects of print quality, these measures are also modelled with reasonable accuracy using paper and press parameters. The models of objective  measures are shown to reveal relationships consistent to known print quality phenomena.

    In the second part of this thesis the application area of anomaly detection in smart homes is explored. A method for modelling human behaviour patterns is proposed. The model is used in order to detect deviating behaviour patterns using contextual information from both time and space. The proposed behaviour pattern model is tested using simulated data and is shown to be suitable given four types of scenarios.

    The thesis shows that parts of offset lithographic printing, which traditionally is a human-centered process, can be automated by the introduction of image processing and machine learning methods. Moreover, it is concluded that in order to facilitate robust and accurate anomaly detection in smart homes, a holistic approach which makes use of several contextual aspects is required.

  • 4.
    Lundström, Jens
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Detecting and exploring deviating behaviour of people in their own homesManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. It is believed that such systems will be very important in ambient assisted living services. Three types of deviations are considered in this work: deviation in activity intensity, deviation in time and deviation in space. Detection of deviations in activity intensity is formulated as the on-line quickest detection of a parameter shift in a sequence of independent Poisson random variables. Random forests trained in an unsupervised fashion are used to learn the spatial and temporal structure of data representing normal behaviour and are thereafter utilised to find deviations.The experimental investigations have shown that the Page and Shiryaev change-point detection methods are preferable in terms of expected delay of motivated alarm. Interestingly only a little is lost when the methods are specified with estimated intensity parameters rather than the true intensity values which are not available in a real situation. As to the spatial and temporal deviations, they can be revealed through analysis of a 2D map of high dimensional data. It was demonstrated that such a map is stable in terms of the number of clusters formed. We have shown that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree.

  • 5.
    Lundström, Jens
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Detecting and exploring deviating behaviour of smart home residents2016Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 55, s. 429-440Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.

    Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.

    The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.

  • 6.
    Lundström, Jens
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Ourique de Morais, Wagner
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Cooney, Martin
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    A Holistic Smart Home Demonstrator for Anomaly Detection and Response2015Ingår i: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, s. 330-335Konferensbidrag (Refereegranskat)
    Abstract [en]

    Applying machine learning methods in scenarios involving smart homes is a complex task. The many possible variations of sensors, feature representations, machine learning algorithms, middle-ware architectures, reasoning/decision schemes, and interactive strategies make research and development tasks non-trivial to solve.In this paper, the use of a portable, flexible and holistic smart home demonstrator is proposed to facilitate iterative development and the acquisition of feedback when testing in regard to the above-mentioned issues. Specifically, the focus in this paper is on scenarios involving anomaly detection and response. First a model for anomaly detection is trained with simulated data representing a priori knowledge pertaining to a person living in an apartment. Then a reasoning mechanism uses the trained model to infer and plan a reaction to deviating activities. Reactions are carried out by a mobile interactive robot to investigate if a detected anomaly constitutes a true emergency. The implemented demonstrator was able to detect and respond properly in 18 of 20 trials featuring normal and deviating activity patterns, suggesting the feasibility of the proposed approach for such scenarios. © IEEE 2015

  • 7.
    Lundström, Jens
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Ourique de Morais, Wagner
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Menezes, Maria Luiza Recena
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Gabrielli, C.
    Bentes, João
    School of Computing and Mathematics, University of Ulster, Shore Road, Jordanstown, Newtownabbey, Co. Antrim, United Kingdom.
    Pinheiro Sant'Anna, Anita
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Synnott, Jonathan
    School of Computing and Mathematics, University of Ulster, Shore Road, Jordanstown, Newtownabbey, Co. Antrim, United Kingdom.
    Nugent, Christopher
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Halmstad intelligent home - Capabilities and opportunities2016Ingår i: Internet of Things Technologies for HealthCare: Third International Conference, HealthyIoT 2016, Västerås, Sweden, October 18-19, 2016, Revised Selected Papers / [ed] Mobyen Uddin AhmedShahina BegumWasim Raad, Berlin: Springer Berlin/Heidelberg, 2016, Vol. 187, s. 9-15Konferensbidrag (Refereegranskat)
    Abstract [en]

    Research on intelligent environments, such as smart homes, concerns the mechanisms that intelligently orchestrate the pervasive technical infrastructure in the environment. However, significant challenges are to build, configure, use and maintain these systems. Providing personalized services while preserving the privacy of the occupants is also difficult. As an approach to facilitate research in this area, this paper presents the Halmstad Intelligent Home and a novel approach for multioccupancy detection utilizing the presented environment. This paper also presents initial results and ongoing work. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.

  • 8.
    Lundström, Jens
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Synnott, Jonathan
    Ulster University, Jordanstown, United Kingdom.
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nugent, Christopher
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Ulster University, Jordanstown, United Kingdom.
    Smart Home Simulation using Avatar Control and Probabilistic Sampling2015Ingår i: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, s. 336-341Konferensbidrag (Refereegranskat)
    Abstract [en]

    Development, testing and validation of algorithms for smart home applications are often complex, expensive and tedious processes. Research on simulation of resident activity patterns in Smart Homes is an active research area and facilitates development of algorithms of smart home applications. However, the simulation of passive infrared (PIR) sensors is often used in a static fashion by generating equidistant events while an intended occupant is within sensor proximity. This paper suggests the combination of avatar-based control and probabilistic sampling in order to increase realism of the simulated data. The number of PIR events during a time interval is assumed to be Poisson distributed and this assumption is used in the simulation of Smart Home data. Results suggest that the proposed approach increase realism of simulated data, however results also indicate that improvements could be achieved using the geometric distribution as a model for the number of PIR events during a time interval. © IEEE 2015

  • 9.
    Lundström, Jens
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Assessing print quality by machine in offset colour printing2013Ingår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 37, s. 70-79Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Information processing steps in printing industry are highly automated, except the last one print quality assessment, which usually is a manual, tedious, and subjective procedure. This article presents a random forests-based technique for automatic print quality assessment based on objective values of several printquality attributes. Values of the attributes are obtained from soft sensors through data mining and colour image analysis. Experimental investigations have shown good correspondence between print quality evaluations obtained by the technique proposed and the average observer. (C) 2012 Elsevier B.V. All rights reserved.

  • 10.
    Lundström, Jens
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Verikas, Antanas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Detecting Halftone Dots for Offset Print Quality Assessment Using Soft Computing2010Ingår i: 2010 IEEE International Conference on Fuzzy Systems (FUZZ), Piscataway, NJ: IEEE Press, 2010, s. 1145-1151Konferensbidrag (Refereegranskat)
    Abstract [en]

    Nowadays in printing industry most of information processing steps are highly automated, except the last one–print quality assessment and control. We present a way to assess one important aspect of print quality, namely the distortion of halftone dots printed colour pictures are made of. The problem is formulated as assessing the distortion of circles detected in microscale images of halftone dot areas. In this paper several known circle detection techniques are explored in terms of accuracy and robustness. We also present a new circle detection technique based on the fuzzy Hough transform (FHT) extended with k-means clustering for detecting positions of accumulator peaks and with an optional fine-tuning step implemented through unsupervised learning. Prior knowledge about the approximate positions and radii of the circles is utilized in the algorithm. Compared to FHT the proposed technique is shown to increase the estimation accuracy of the position and size of detected circles. The techniques are investigated using synthetic and natural images.

  • 11.
    Lundström, Jens
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Verikas, Antanas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    System for Assessing, Exploring and Monitoring Offset Print Quality2011Ingår i: Recent Researches in Circuits, Systems, Communications & Computers: Proc. of 2nd European Conference of Communications (ECCOM'11), Athens: World Scientific and Engineering Academy and Society, 2011, s. 28-33Konferensbidrag (Refereegranskat)
    Abstract [en]

    Variations in offset print quality relate to numerous parameter of printing press and paper. To maintain constant quality of products, press operators need to assess, explore and monitor print quality. This paper presents a novel system for assessing and predicting values of print quality attributes, where the adopted, random forests (RF)-based, modeling approach also allows quantifying the influence of different parameters. In contrast to other print quality assessment systems, this system utilizes common print marks known as double grey-bars. A novel virtual sensor for assessing the mis-registration degree of printing plates using images of double grey-bars is presented. The inferred influence of paper and printing press parameters on print quality shows correlation with known print quality conditions.

  • 12.
    Lundström, Jens
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system. Kaunas University of Technology, Kaunas, Lithuania.
    Tullander, E.
    Hylte Mill, Hyltebruk, Sweden.
    Larsson, B.
    V-TAB, Hisingsbacka, Sweden.
    Assessing, exploring, and monitoring quality of offset colour prints2013Ingår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 46, nr 4, s. 1427-1441Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Variations in offset print quality relate to numerous parameters of printing press and paper. To maintain a constant high print quality press operators need to assess, explore and monitor quality of prints. Today assessment is mainly done manually. This paper presents a novel system for assessing and predicting values of print quality attributes, where the adopted, random forests (RFs)-based, modeling approach also allows quantifying the influence of different paper and press parameters on print quality. In contrast to other print quality assessment systems the proposed system utilises common, simple print marks known as double grey-bars. Novel virtual sensors assessing print quality attributes using images of double grey-bars are presented. The inferred influence of paper and printing press parameters on quality of colour prints shows clear relation with known print quality conditions. Thorough analysis and categorisation of related work is also given in the paper. (C) 2012 Elsevier Ltd. All rights reserved.

  • 13.
    Norell Pejner, Margaretha
    et al.
    Högskolan i Halmstad, Akademin för hälsa och välfärd, Centrum för forskning om välfärd, hälsa och idrott (CVHI).
    Lundström, Jens
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Ourique de Morais, Wagner
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Laurell, Hélène
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Centrum för innovations-, entreprenörskaps- och lärandeforskning (CIEL), Centre for International Marketing and Entrepreneurship Research (CIMER).
    Isaksson, Anna
    Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, Centrum för lärande, kultur och samhälle (CLKS), Språk, kultur och samhälle.
    Stranne, Frida
    Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, Centrum för lärande, kultur och samhälle (CLKS).
    Skärsäter, Ingela
    Högskolan i Halmstad, Akademin för hälsa och välfärd, Centrum för forskning om välfärd, hälsa och idrott (CVHI).
    Smart medication organizer – one way to promote self-management and safety in drug administration in elderly people2017Konferensbidrag (Refereegranskat)
  • 14.
    Nugent, Christopher
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). University of Ulster, Jordanstown, North Ireland.
    Synnott, Jonathan
    University of Ulster, Jordanstown, North Ireland.
    Gabrielli, Celeste
    Marche Polytechnic University, Ancona, Italy.
    Zhang, Shuai
    University of Ulster, Jordanstown, North Ireland.
    Espinilla, Macarena
    University of Jaén, Jaen, Spain..
    Calzada, Alberto
    University of Ulster, Jordanstown, North Ireland.
    Lundström, Jens
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Cleland, Ian
    University of Ulster, Jordanstown, North Ireland.
    Synnes, Kare
    Luleå university of Technology, Luleå, Sweden.
    Hallberg, Josef
    Luleå university of Technology, Luleå, Sweden.
    Spinsante, Susanna
    Marche Polytechnic University, Ancona, Italy.
    Ortiz Barrios, Miguel Angel
    Universidad de la Costa CUC, Barranquilla, Colombia.
    Improving the Quality of User Generated Data Sets for Activity Recognition2016Ingår i: Ubiquitous Computing and Ambient Intelligence, UCAMI 2016, PT II / [ed] Garcia, CR CaballeroGil, P Burmester, M QuesadaArencibia, A, Amsterdam: Springer Publishing Company, 2016, s. 104-110Konferensbidrag (Refereegranskat)
    Abstract [en]

    It is fully appreciated that progress in the development of data driven approaches to activity recognition are being hampered due to the lack of large scale, high quality, annotated data sets. In an effort to address this the Open Data Initiative (ODI) was conceived as a potential solution for the creation of shared resources for the collection and sharing of open data sets. As part of this process, an analysis was undertaken of datasets collected using a smart environment simulation tool. A noticeable difference was found in the first 1-2 cycles of users generating data. Further analysis demonstrated the effects that this had on the development of activity recognition models with a decrease of performance for both support vector machine and decision tree based classifiers. The outcome of the study has led to the production of a strategy to ensure an initial training phase is considered prior to full scale collection of the data.

  • 15.
    Ourique de Morais, Wagner
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Inbyggda system (CERES).
    Lundström, Jens
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Wickström, Nicholas
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    A Database-Centric Architecture for Home-Based Health Monitoring2013Ingår i: Ambient Assisted Living and Active Aging: 5th International Work-Conference, IWAAL 2013, Carrillo, Costa Rica, December 2-6, 2013, Proceedings / [ed] Christopher Nugent, Antonio Coronato, José Bravo, Heidelberg, Germany: Springer, 2013, Vol. 8277, s. 26-34Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Traditionally, database management systems (DBMSs) have been employed exclusively for data management in infrastructures supporting Ambient Assisted Living (AAL) systems. However, DBMSs provide other mechanisms, such as for security, dependability, and extensibility that can facilitate the development, use, and maintenance of AAL applications. This work utilizes such mechanisms, particularly extensibility, and proposes a database-centric architecture to support home-based healthcare applications. An active database is used to monitor and respond to events taking place in the home, such as bed-exits. In-database data mining methods are applied to model early night behaviors of people living alone. Encapsulating the processing into the DBMS avoids transferring and processing sensitive data outside of database, enables changes in the logic to be managed on-the-fly, and reduces code duplication. As a result, such an approach leads to better performance and increased security and privacy, and can facilitate the adaptability and scalability of AAL systems. An evaluation of the architecture with datasets collected in real homes demonstrated the feasibility and flexibility of the approach.

  • 16.
    Ourique de Morais, Wagner
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Inbyggda system (CERES).
    Lundström, Jens
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Wickström, Nicholas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Active In-Database Processing to Support Ambient Assisted Living Systems2014Ingår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, nr 8, s. 14765-14785Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As an alternative to the existing software architectures that underpin the development of smart homes and ambient assisted living (AAL) systems, this work presents a database-centric architecture that takes advantage of active databases and in-database processing. Current platforms supporting AAL systems use database management systems (DBMSs) exclusively for data storage. Active databases employ database triggers to detect and react to events taking place inside or outside of the database. DBMSs can be extended with stored procedures and functions that enable in-database processing. This means that the data processing is integrated and performed within the DBMS. The feasibility and flexibility of the proposed approach were demonstrated with the implementation of three distinct AAL services. The active database was used to detect bed-exits and to discover common room transitions and deviations during the night. In-database machine learning methods were used to model early night behaviors. Consequently, active in-database processing avoids transferring sensitive data outside the database, and this improves performance, security and privacy. Furthermore, centralizing the computation into the DBMS facilitates code reuse, adaptation and maintenance. These are important system properties that take into account the evolving heterogeneity of users, their needs and the devices that are characteristic of smart homes and AAL systems. Therefore, DBMSs can provide capabilities to address requirements for scalability, security, privacy, dependability and personalization in applications of smart environments in healthcare.

  • 17.
    Pejner, Norell Margaretha
    et al.
    Högskolan i Halmstad, Akademin för hälsa och välfärd, Centrum för forskning om välfärd, hälsa och idrott (CVHI).
    Ourique de Morais, Wagner
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Lundström, Jens
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Laurell, Hélène
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Centrum för innovations-, entreprenörskaps- och lärandeforskning (CIEL).
    Skärsäter, Ingela
    Högskolan i Halmstad, Akademin för hälsa och välfärd, Centrum för forskning om välfärd, hälsa och idrott (CVHI).
    A Smart Home System for Information Sharing, Health Assessments, and Medication Self-Management for Older People: Protocol for a Mixed-Methods Study2019Ingår i: JMIR Research Protocols, ISSN 1929-0748, E-ISSN 1929-0748, Vol. 8, nr 4, artikel-id e12447Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Older adults often want to stay in a familiar place, such as their home, as they get older. This so-called aging in place, which may involve support from relatives or care professionals, can promote older people’s independence and well-being. The combination of aging and disease, however, can lead to complex medication regimes, and difficulties for care providers in correctly assessing the older person's health. In addition, the organization of the health care is fragmented, which makes it difficult for health professionals to encourage older people to participate in their care. It is also a challenge to perform adequate health assessment and appropriate communication between health care professionals.

    Objective: The purpose of this paper is to describe the design for an integrated home-based system that can acquire and compile health-related evidence for guidance and information sharing among care providers and care receivers in order to support and promote medication self-management among older people.

    Methods: The authors used a participatory design (PD) approach for this mixed-method project, which was divided into four phases: Phase I, Conceptualization, consisted of the conceptualization of a system to support medication self- management, objective health assessments, and communication between health care professionals. Phase II, Development of a System, consisted of building and bringing together the conceptualized systems from phase I. Phases III (pilot study) and IV (a full-scale study) are described briefly.

    Results: Our participants in phase I were people who were involved in some way in the care of older adults, and included older adults themselves, relatives of older adults, care professionals, and industrial partners. With input from phase I participants, we identified two relevant concepts for promoting medication self-management, both of which related to systems that participants believed could provide guidance for the older adults themselves, relatives of older adults, and care professionals. The system will also encourage information sharing between care providers and care receivers. The first is the concept of the Intelligent Friendly Home (IAFH), defined as an integrated residential system that evolves to sense, reason and act in response to individual needs, preferences and behaviors as these change over time. The second concept is the MedOP system, a system that would be supported by the IAFH, and which consists of three related components: one that assess health behaviors, another that communicates health data, and a third that promotes medication self-management.

    Conclusions: The participants in this project were older adults, relatives of older adults, care professionals, and our industrial partners. With input from the participants, we identified two main concepts that could comprise a system for health assessment, communication and medication self-management: the Intelligent Friendly Home (IAFH), and the MedOP system. These concepts will be tested in this study to determine whether they can facilitate and promote medication self-management in older people. © The authors. All rights reserved. 

  • 18.
    Spinsante, Susanna
    et al.
    Universita’ Politecnica delle Marche, Ancona, Italy.
    Angelici, Alberto
    Universita’ Politecnica delle Marche, Ancona, Italy.
    Lundström, Jens
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Espinilla, Macarena
    University of Jaen, Jaen, Spain.
    Cleland, Ian
    University of Ulster, Newtownabbey, Ulster, United Kingdom.
    Nugent, Christopher
    University of Ulster, Newtownabbey, Ulster, United Kingdom.
    A Mobile Application for Easy Design and Testing of Algorithms to Monitor Physical Activity in the Workplace2016Ingår i: International Journal of Mobile Information Systems, ISSN 1574-017X, E-ISSN 1875-905X, artikel-id 5126816Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available on-board accelerometer sensor. In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like inmodern workplace environments. Increased sitting time is significantly associated with severe health diseases, and the workplace is an appropriate intervention setting, due to the sedentary behavior typical of modern jobs. Within this paper, the state-of-the-art components of HAR are analyzed, in order to identify and select the most effective signal filtering and windowing solutions for physical activity monitoring. The classifier development process is based upon three phases; a feature extraction phase, a feature selection phase, and a training phase. In the training phase, a publicly available dataset is used to test among different classifier types and learning methods. A user-friendly Android-based smartphone application with low computational requirements has been developed to run field tests, which allows to easily change the classifier under test, and to collect new datasets ready for use with machine learning APIs. The newly created datasets may include additional information, like the smartphone position, its orientation, and the user's physical characteristics. Using the mobile tool, a classifier based on a decision tree is finally set up and enriched with the introduction of some robustness improvements. The developed approach is capable of classifying six activities, and to distinguish between not active (sitting) and active states, with an accuracy near to 99%. The mobile tool, which is going to be further extended and enriched, will allow for rapid and easy benchmarking of new algorithms based on previously generated data, and on future collected datasets. © 2016 Susanna Spinsante et al.

  • 19.
    Synnott, Jonathan
    et al.
    University of Ulster, Jordanstown, North Ireland.
    Nugent, Chris
    Univ Ulster, Sch Comp & Math, Jordanstown, North Ireland..
    Zhang, Shuai
    Univ Ulster, Sch Comp & Math, Jordanstown, North Ireland..
    Calzada, Alberto
    Univ Ulster, Sch Comp & Math, Jordanstown, North Ireland..
    Cleland, Ian
    Univ Ulster, Sch Comp & Math, Jordanstown, North Ireland..
    Espinilla, Macarena
    Univ Jaen, Dept Comp Sci, Jaen, Spain..
    Medina Quero, Javier
    Univ Jaen, Dept Comp Sci, Jaen, Spain..
    Lundström, Jens
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Environment Simulation for the Promotion of the Open Data Initiative2016Ingår i: 2016 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP), Piscataway, N.J.: IEEE, 2016, s. 246-251Konferensbidrag (Refereegranskat)
    Abstract [en]

    The development, testing and evaluation of novel approaches to Intelligent Environment data processing require access to datasets which are of high quality, validated and annotated. Access to such datasets is limited due to issues including cost, flexibility, practicality, and a lack of a globally standardized data format. These limitations are detrimental to the progress of research. This paper provides an overview of the Open Data Initiative and the use of simulation software (IE Sim) to provide a platform for the objective assessment and comparison of activity recognition solutions. To demonstrate the approach, a dataset was generated and distributed to 3 international research organizations. Results from this study demonstrate that the approach is capable of providing a platform for benchmarking and comparison of novel approaches.

  • 20.
    Uličný, Matej
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Lundström, Jens
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Robustness of Deep Convolutional Neural Networks for Image Recognition2016Ingår i: Intelligent Computing Systems: First International Symposium, ISICS 2016, Mérida, México, March 16-18, 2016, Proceedings / [ed] Anabel Martin-Gonzalez, Victor Uc-Cetina, Cham: Springer, 2016, Vol. 597, s. 16-30Konferensbidrag (Refereegranskat)
    Abstract [en]

    Recent research has found deep neural networks to be vulnerable, by means of prediction error, to images corrupted by small amounts of non-random noise. These images, known as adversarial examples are created by exploiting the input to output mapping of the network. For the MNIST database, we observe in this paper how well the known regularization/robustness methods improve generalization performance of deep neural networks when classifying adversarial examples and examples perturbed with random noise. We conduct a comparison of these methods with our proposed robustness method, an ensemble of models trained on adversarial examples, able to clearly reduce prediction error. Apart from robustness experiments, human classification accuracy for adversarial examples and examples perturbed with random noise is measured. Obtained human classification accuracy is compared to the accuracy of deep neural networks measured in the same experimental settings. The results indicate, human performance does not suffer from neural network adversarial noise.

  • 21.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Lundström, Jens
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Bacauskiene, Marija
    Department of Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Advances in computational intelligence-based print quality assessment and control in offset colour printing2011Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 38, nr 10, s. 13441-13447Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Nowadays most of information processing steps in printing industry are highly automated, except the last one – print quality assessment and control. Usually quality assessment is a manual, tedious, and subjective procedure. This article presents a survey of non numerous developments in the field of computational intelligence-based print quality assessment and control in offset colour printing. Recent achievements in this area and advances in applied computational intelligence, expert and decision support systems lay good foundations for creating practical tools to automate the last step of the printing process.

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