hh.sePublications
Change search
Refine search result
1 - 34 of 34
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Ashfaq, Awais
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Halland Hospital, Region Halland, Sweden.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Lingman, Markus
    Halland Hospital, Region Halland, Sweden & Institute of Medicine, Dept. of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Readmission prediction using deep learning on electronic health records2019In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 97, article id 103256Article in journal (Refereed)
    Abstract [en]

    Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted intervention programs for patients at risk of readmission. This requires identifying high-risk patients at the time of discharge from hospital. Here, using real data from over 7,500 CHF patients hospitalized between 2012 and 2016 in Sweden, we built and tested a deep learning framework to predict 30-day unscheduled readmission. We present a cost-sensitive formulation of Long Short-Term Memory (LSTM) neural network using expert features and contextual embedding of clinical concepts. This study targets key elements of an Electronic Health Record (EHR) driven prediction model in a single framework: using both expert and machine derived features, incorporating sequential patterns and addressing the class imbalance problem. We show that the model with all key elements achieves a higher discrimination ability (AUC 0.77) compared to the rest. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients. © 2019 The Authors

  • 2.
    Blom, Mathias Carl
    et al.
    Department of Clinical Sciences, Lund University, Lund, Sweden.
    Ashfaq, Awais
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Halland Hospital, Region Halland, Halmstad, Sweden.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Anderson, Philip D.
    Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA & Harvard Medical School, Boston, Massachusetts, USA.
    Lingman, Markus
    Halland Hospital, Region Halland, Sweden & Department of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study2019In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 9, no 8, article id e028015Article in journal (Refereed)
    Abstract [en]

    Background: Aggressive treatment at end-of-life (EOL) can be traumatic to patients and may not add clinical benefit. Absent an accurate prognosis of death, individual level biases may prevent timely discussions about the scope of EOL care and patients are at risk of being subject to care against their desire. The aim of this work is to develop predictive algorithms for identifying patients at EOL, with clinically meaningful discriminatory power.

    Methods: Retrospective, population-based study of patients utilizing emergency departments (EDs) in Sweden, Europe. Electronic health records (EHRs) were used to train supervised learning algorithms to predict all-cause mortality within 30 days following ED discharge. Algorithm performance was validated out of sample on EHRs from a separate hospital, to which the algorithms were previously unexposed.

    Results: Of 65,776 visits in the development set, 136 (0.21%) experienced the outcome. The algorithm with highest discrimination attained ROC-AUC 0.945 (95% CI 0.933 - 0.956), with sensitivity 0.869 (95% CI 0.802, 0.931) and specificity 0.858 (0.855, 0.860) on the validation set.

    Conclusions: Multiple algorithms displayed excellent discrimination and outperformed available indexes for short-term mortality prediction. The practical utility of the algorithms increases as the required data were captured electronically and did not require de novo data collection.

    Trial registration number: Not applicable.

  • 3.
    Calikus, Ece
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Ranking Abnormal Substations by Power Signature Dispersion2018In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 149, p. 345-353Article in journal (Refereed)
    Abstract [en]

    The relation between heat demand and outdoor temperature (heat power signature) is a typical feature used to diagnose abnormal heat demand. Prior work is mainly based on setting thresholds, either statistically or manually, in order to identify outliers in the power signature. However, setting the correct threshold is a difficult task since heat demand is unique for each building. Too loose thresholds may allow outliers to go unspotted, while too tight thresholds can cause too many false alarms.

    Moreover, just the number of outliers does not reflect the dispersion level in the power signature. However, high dispersion is often caused by fault or configuration problems and should be considered while modeling abnormal heat demand.

    In this work, we present a novel method for ranking substations by measuring both dispersion and outliers in the power signature. We use robust regression to estimate a linear regression model. Observations that fall outside of the threshold in this model are considered outliers. Dispersion is measured using coefficient of determination R2 which is a statistical measure of how close the data are to the fitted regression line.

    Our method first produces two different lists by ranking substations using number of outliers and dispersion separately. Then, we merge the two lists into one using the Borda Count method. Substations appearing on the top of the list should indicate higher abnormality in heat demand compared to the ones on the bottom. We have applied our model on data from substations connected to two district heating networks in the south of Sweden. Three different approaches i.e. outlier-based, dispersion-based and aggregated methods are compared against the rankings based on return temperatures. The results show that our method significantly outperforms the state-of-the-art outlier-based method. © 2018 The Authors. Published by Elsevier Ltd.

  • 4.
    Camelo, Guilherme Antonio
    et al.
    Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
    Recena Menezes, Maria Luiza
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS). Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Vicari, Rosa Maria
    Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
    Pereira, Carlos Eduardo
    Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
    Control of Smart Environments Using Brain Computer Interface Based on Genetic Algorithm2016In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 9622, p. 773-781Article in journal (Refereed)
    Abstract [en]

    This work deals with the development of an interface to control a smart conference room using passive BCI (Brain Computer Interface). It compares a genetic algorithm developed in a previous project to control the smart conference room with a random control algorithm. The system controls features of the conference room such as air conditioner, lightning systems, electric shutters, entertainment devices, etc. The parameters of the algorithm are extracted from users biosignal using Emotiv Epoc Headset while the user performs an attention test. The tests indicate that the decisions made by the genetic algorithm lead to better results, but in a single execution cannot be considered an effective optimization algorithm. © Springer-Verlag Berlin Heidelberg 2016.

  • 5.
    Cooney, Martin
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Fan, Yuantao
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pitfalls of Affective Computing: How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?2018In: WWW '18 Companion Proceedings of the The Web Conference 2018, New York, NY: ACM Publications, 2018, p. 1563-1566Conference paper (Refereed)
    Abstract [en]

    What would happen in a world where people could "see'' others' hidden emotions directly through some visualizing technology Would lies become uncommon and would we understand each other better Or to the contrary, would such forced honesty make it impossible for a society to exist The science fiction television show Black Mirror has exposed a number of darker scenarios in which such futuristic technologies, by blurring the lines of what is private and what is not, could also catalyze suffering. Thus, the current paper first turns an eye towards identifying some potential pitfalls in emotion visualization which could lead to psychological or physical harm, miscommunication, and disempowerment. Then, some countermeasures are proposed and discussed--including some level of control over what is visualized and provision of suitably rich emotional information comprising intentions--toward facilitating a future in which emotion visualization could contribute toward people's well-being. The scenarios presented here are not limited to web technologies, since one typically thinks about emotion recognition primarily in the context of direct contact. However, as interfaces develop beyond today's keyboard and monitor, more information becomes available also at a distance--for example, speech-to-text software could evolve to annotate any dictated text with a speaker's emotional state.

  • 6.
    Cooney, Martin
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Avoiding Playfulness Gone Wrong: Exploring Multi-objective Reaching Motion Generation in a Social Robot2017In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 9, no 4, p. 545-562Article in journal (Refereed)
    Abstract [en]

    Companion robots will be able to perform useful tasks in homes and public places, while also providing entertainment through playful interactions. “Playful” here means fun, happy, and humorous. A challenge is that generating playful motions requires a non-trivial understanding of how people attribute meaning and intentions. The literature suggests that playfulness can lead to some undesired impressions such as that a robot is obnoxious, untrustworthy, unsafe, moving in a meaningless fashion, or boring. To generate playfulness while avoiding such typical failures, we proposed a model for the scenario of a robot arm reaching for an object: some simplified movement patterns such as sinusoids are structured toward appearing helpful, clear about goals, safe, and combining a degree of structure and anomaly. We integrated our model into a mathematical framework (CHOMP) and built a new robot, Kakapo, to perform dynamically generated motions. The results of an exploratory user experiment were positive, suggesting that: Our proposed system was perceived as playful over the course of several minutes. Also a better impression resulted compared with an alternative playful system which did not use our proposed heuristics; furthermore a negative effect was observed for several minutes after showing the alternative motions, suggesting that failures are important to avoid. And, an inverted u-shaped correlation was observed between motion length and degree of perceived playfulness, suggesting that motions should neither be too short or too long and that length is also a factor which can be considered when generating playful motions. A short follow-up study provided some additional support for the idea that playful motions which seek to avoid failures can be perceived positively. Our intent is that these exploratory results will provide some insight for designing various playful robot motions, toward achieving some good interactions. © 2017, The Author(s).

  • 7.
    de Morais, Wagner Ourique
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    A Wearable Accelerometer Based Platform to Encourage Physical Activity for the Elderly2008In: Gerontechnology : international journal on the fundamental aspects of technology to serve the ageing society, ISSN 1569-1101, Vol. 07, no 02, p. 129-181Article in journal (Other academic)
    Abstract [en]

    The growth in the elderly population will pose great pressure on the healthcare system to treat common geriatric problem. Preventive approaches like encouraging elderly people to perform physical exercises can decrease the risk of developing chronic diseases. In cases when diseases already have developed, further developments could possibly be retarded. In this work a wearable platform to recognize user’ s    movements    presented.    The    platform    provides interactions with simple computer games designed to promote physical activity.

  • 8.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Ourique de Morais, Wagner
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Menezes, Maria Luiza Recena
    Halmstad University, School of Information Technology, 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
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Synnott, Jonathan
    School of Computing and Mathematics, University of Ulster, Shore Road, Jordanstown, Newtownabbey, Co. Antrim, United Kingdom.
    Nugent, Christopher
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Halmstad intelligent home - Capabilities and opportunities2016In: 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, p. 9-15Conference paper (Refereed)
    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.

  • 9.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Laso, A.
    Department of Electrical and Energy Engineering, University of Cantabria, Santander, Spain.
    Manana, M.
    Department of Electrical and Energy Engineering, University of Cantabria, Santander, Spain.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Stream Data Cleaning for Dynamic Line Rating Application2018In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 11, no 8, article id 2007Article in journal (Refereed)
    Abstract [en]

    The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination of (a) a set of constraints obtained from derivatives in consecutive data, and (b) association rules that are automatically generated from historical data. In smart grids, a large amount of historical data from different weather stations are available but rarely used. In this work, we show that mining and analyzing this historical data provides valuable information that can be used for detecting and replacing faulty sensor readings. We compare the result of the proposed method against the exponentially weighted moving average and vector autoregression models. Experiments on data sets with real and synthetic errors demonstrate the good performance of the proposed method for monitoring weather sensors.

  • 10.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Analyzing and Modeling Propagation of SmartMeters' Alarms in Low-Voltage GridsIn: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756XArticle in journal (Refereed)
    Abstract [en]

    In low-voltage energy distribution networks, analyzing and modelingpropagation of disturbances is important as we are moving towards smartgrids. Today, smart meters are generally deployed at all customers and continuouslymeasure several features related to power consumption and quality.However, the data collected from such low-cost devices has been, until now,considered unsuitable for analysis of disturbance propagation, mainly due to itsvery low time resolution. This paper demonstrates that the existence of propagationin the low-voltage grids can be detected using smart meters alarm data.In particular, several models for propagation of disturbances, within neighborcustomers in different levels of the grid topology, are investigated. A methodfor measuring how the reality corresponds to each of the models, by measuringthe similarity between real data and synthetic data, is proposed. Theresults show that the models which include propagation within both deliverypointsand branches are better representation of the disturbances in the realdata, compared to other models. Furthermore, the paper presents smart metersalarm dataset (SMAData), an open-source dataset containing power qualitydisturbances of over 1000 customers.

  • 11.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Jürgensen, Jan Henning
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Hilber, Patrik
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Reliability Evaluation of Power Cables Considering the Restoration Characteristic2019In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 105, p. 622-631Article in journal (Refereed)
    Abstract [en]

    In this paper Weibull parametric proportional hazard model (PHM) is used to estimate the failure rate of every individual cable based on its age and a set of explanatory factors. The required information for the proposed method is obtained by exploiting available historical cable inventory and failure data. This data-driven method does not require any additional measurements on the cables, and allows the cables to be ranked for maintenance prioritization and repair actions.

    Furthermore, the results of reliability analysis of power cables are compared when the cables are considered as repairable or non-repairable components. The paper demonstrates that the methods which estimate the time-to-the-first failure (for non-repairable components) lead to incorrect conclusions about reliability of repairable power cables.

    The proposed method is used to evaluate the failure rate of each individual Paper Insulated Lead Cover (PILC) underground cables in a distribution grid in the south of Sweden. © 2018 Elsevier Ltd

  • 12.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sant´Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids2016In: 2016 IEEE International Energy Conference (ENERGYCON), 2016Conference paper (Refereed)
    Abstract [en]

    The diversity of components in electricity distribution grids makes it impossible, or at least very expensive, to deploy monitoring and fault diagnostics to every individual element. Therefore, power distribution companies are looking for cheap and reliable approaches that can help them to estimate the condition of their assets and to predict the when and where the faults may occur. In this paper we propose a simplified representation of failure patterns within historical faults database, which facilitates visualization of association rules using Bayesian Networks. Our approach is based on exploring the failure history and detecting correlations between different features available in those records. We show that a small subset of the most interesting rules is enough to obtain a good and sufficiently accurate approximation of the original dataset. A Bayesian Network created from those rules can serve as an easy to understand visualization of the most relevant failure patterns. In addition, by varying the threshold values of support and confidence that we consider interesting, we are able to control the tradeoff between accuracy of the model and its complexity in an intuitive way. © 2016 IEEE

  • 13.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Overview of Smart Grid Challenges in Sweden2014In: The SAIS Workshop 2014 Proceedings, Swedish Artificial Intelligence Society (SAIS) , 2014, p. 155-164Conference paper (Refereed)
    Abstract [en]

    Smart grids are advanced power grids that use modern hardware and software technologies to provide clean, safe, secure, reliable, ecient and sustainable energy. However, there are many challenges in the eld of smart grids in terms of communication, reliability, interoperability, and big data that should be considered. In this paper we present a brief overview of some of the challenges and solutions in the smart grids, focusing especially on the Swedish point of view. We discuss thirty articles, from 2006 until 2013, with the main interest on datarelated challenges.

  • 14.
    Mashad Nemati, Hassan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Reliability Evaluation of Underground Power Cables with Probabilistic Models2015In: DMIN'15: The 2015 International Conference on Data Mining, 2015, p. 37-43Conference paper (Refereed)
    Abstract [en]

    Underground power cables are one of the fundamental elements in power grids, but also one of the more difficult ones to monitor. Those cables are heavily affected by ionization, as well as thermal and mechanical stresses. At the same time, both pinpointing and repairing faults is very costly and time consuming. This has caused many power distribution companies to search for ways of predicting cable failures based on available historical data.

    In this paper, we investigate five different models estimating the probability of failures for in-service underground cables. In particular, we focus on a methodology for evaluating how well different models fit the historical data. In many practical cases, the amount of data available is very limited, and it is difficult to know how much confidence should one have in the goodness-of-fit results.

    We use two goodness-of-fit measures, a commonly used one based on mean square error and a new one based on calculating the probability of generating the data from a given model. The corresponding results for a real data set can then be interpreted by comparing against confidence intervals obtained from synthetic data generated according to different models.

    Our results show that the goodness-of-fit of several commonly used failure rate models, such as linear, piecewise linear and exponential, are virtually identical. In addition, they do not explain the data as well as a new model we introduce: piecewise constant.

  • 15.
    Menezes, Maria Luiza Recena
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Methodology for Subject Authentification and Identification through EEG signal: equipment's and positioning artifacts2018In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 37-37Conference paper (Refereed)
  • 16.
    Menezes, Maria Luiza Recena
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pavel, Misha
    Northeastern University, Boston, USA.
    Jimison, Holly
    Northeastern University, Boston, USA.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Affective Ambient Intelligence: from Domotics to Ambient Intelligence2018In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 25-25Conference paper (Refereed)
  • 17.
    Menezes, Maria Luiza Recena
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Samara, A.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Galway, L.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Alonso-Fernandez, Fernando
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wang, H.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Bond, R.
    School of Computing and Mathematics, Ulster University Belfast, Belfast, United Kingdom.
    Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset2017In: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 21, no 6, p. 1003-1013Article in journal (Refereed)
    Abstract [en]

    One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user’s emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell’s Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the and waves and High Order Crossing of the EEG signal. © 2017, The Author(s).

  • 18.
    Nowaczyk, Sławomir
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Calikus, Ece
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Fan, Yuantao
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Monitoring equipment operation through model and event discovery2018In: Intelligent Data Engineering and Automated Learning – IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II / [ed] Hujun Yin, David Camacho Paulo Novais & Antonio J. Tallón-Ballesteros, Cham: Springer, 2018, Vol. 11315, p. 41-53Conference paper (Refereed)
    Abstract [en]

    Monitoring the operation of complex systems in real-time is becoming both required and enabled by current IoT solutions. Predicting faults and optimising productivity requires autonomous methods that work without extensive human supervision. One way to automatically detect deviating operation is to identify groups of peers, or similar systems, and evaluate how well each individual conforms with the group. We propose a monitoring approach that can construct knowledge more autonomously and relies on human experts to a lesser degree: without requiring the designer to think of all possible faults beforehand; able to do the best possible with signals that are already available, without the need for dedicated new sensors; scaling up to “one more system and component” and multiple variants; and finally, one that will adapt to changes over time and remain relevant throughout the lifetime of the system. © Springer Nature Switzerland AG 2018.

  • 19.
    Sant'Anna, Anita
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    A Symbolic Approach to Human Motion Analysis Using Inertial Sensors: Framework and Gait Analysis Study2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Motion analysis deals with determining what and how activities are being performed by a subject, through the use of sensors. The process of answering the what question is commonly known as classification, and answering the how question is here referred to as characterization. Frequently, combinations of inertial sensor such as accelerometers and gyroscopes are used for motion analysis. These sensors are cheap, small, and can easily be incorporated into wearable systems.

    The overall goal of this thesis was to improve the processing of inertial sensor data for the characterization of movements. This thesis presents a framework for the development of motion analysis systems that targets movement characterization, and describes an implementation of the framework for gait analysis. One substantial aspect of the framework is symbolization, which transforms the sensor data into strings of symbols. Another aspect of the framework is the inclusion of human expert knowledge, which facilitates the connection between data and human concepts, and clarifies the analysis process to a human expert.

    The proposed implementation was compared to state of practice gait analysis systems, and evaluated in a clinical environment. Results showed that expert knowledge can be successfully used to parse symbolic data and identify the different phases of gait. In addition, the symbolic representation enabled the creation of new gait symmetry and gait normality indices. The proposed symmetry index was superior to many others in detecting movement asymmetry in early-to-mid-stage Parkinson's Disease patients. Furthermore, the normality index showed potential in the assessment of patient recovery after hip-replacement surgery. In conclusion, this implementation of the gait analysis system illustrated that the framework can be used as a road map for the development of movement analysis systems.

  • 20.
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Activity monitoring as a tool for person-centered care: preliminary report2014In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) / [ed] Huiru (Jane) Zheng, Werner Dubitzky, Xiaohua Hu, Jin-Kao Hao, Daniel Berrar, Kwang-Hyun Cho, Yadong Wang & David Gilbert, Piscataway, NJ: IEEE Press, 2014, p. 48-51Conference paper (Refereed)
    Abstract [en]

    The Person-Centered Care (PCC) paradigm advocates that instead of being the passive target of a medical intervention, patients should play an active part in their care and in the decision-making process, together with clinicians. Although new mobile and wearable technologies have created a new wave of personalized health-related applications, it is still unclear how these technologies can be used in health care institutions in order to support person-centered care. In order to investigate this matter, we undertook a pilot study aimed at determining if and how activity monitoring can support person-centered care routines for patients undergoing total hip replacement surgery. This is a preliminary report describing the methodology, preliminary results, and some practical challenges. We present here an orientation-invariant, accelerometer-based activity monitoring method, especially designed to address the requirements of monitoring in-patients in a real clinical setting. We also present and discuss some practical issues related to complying with hospital requirements and collaborating with hospital staff. © 2014 IEEE.

  • 21.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Bass, Robert
    Portland State University, Portland, OR, USA.
    A New Two-Degree-of-Freedom Space Heating Model for Demand Response2014In: SMARTGREENS 2014: Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems, [S. l.]: SciTePress, 2014, p. 5-13Conference paper (Refereed)
    Abstract [en]

    In today’s fast changing electric utilities sector demand response (DR) programs are a relatively inexpensive means of reducing peak demand and providing ancillary services. Advancements in embedded systems and communication technologies are paving the way for more complex DR programs based on transactive control. Such complex systems highlight the importance of modeling and simulation tools for studying and evaluating the effects of different control strategies for DR. Considerable efforts have been directed at modeling thermostatically controlled appliances. These models however operate with only one degree of freedom, typically, the thermal mass temperature. This paper proposes a two-degree-of-freedom residential space heating system composed of a thermal storage unit and forced convection system. Simulation results demonstrate that such system is better suited for maintaining thermal comfort and allows greater flexibility for DR programs. The performance of several control strategies are evaluated, as well as the effects of model and weather parameters on thermal comfort and power consumption.

  • 22.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Ourique de Morais, Wagner
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gait Unsteadiness Analysis from Motion Primitives2008In: Gerontechnology : international journal on the fundamental aspects of technology to serve the ageing society, ISSN 1569-1101, Vol. 7, no 2, p. 204-Article in journal (Other academic)
    Abstract [en]

    The development of intelligent ambulatory monitoring systems and smart living environments is important when considering the aging of society and its implications. This work concerns the use of human motion analysis as a tool for supporting elderly life. Movement recognition has so far been achieved through some form of template matching after manual segmentation or modeling of important features. However, previous works have failed to generalize movement and have only been able to recognize few predetermined activities. To cope with those limitations, this work suggests a new “motion language” approach. To demonstrate the viability and usefulness of this methodology, the concept of “motion primitives” was used to quantitatively analyze gait unsteadiness, which relates to physical condition and cognitive performance. The variability of stride time and temporal walk symmetry between the two feet were measured. Accelerometers were chosen as motion sensors since they offer desirable features in monitoring human movements such as response to both movement frequency and intensity, miniaturization and low power consumption. This study shows that a motion language methodology is capable of quantitatively measuring temporal gait characteristics and providing tools for continuous, unobtrusive, home-based gait analysis.

  • 23.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Salarian, Arash
    Oregon Health and Science Univeristy.
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    A new measure of movement symmetry in early Parkinson's disease patients using symbolic processing of inertial sensor data2011In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 58, no 7, p. 2127-2135Article in journal (Refereed)
    Abstract [en]

    Movement asymmetry is one of the motor symptoms associated with Parkinson's Disease (PD). Therefore, being able to detect and measure movement symmetry is important for monitoring the patient's condition.

    The present paper introduces a novel symbol based symmetry index calculated from inertial sensor data. The method is explained, evaluated and compared to six other symmetry measures. These measures were used to determine the symmetry of both upper and lower limbs during walking of 11 early-to-mid-stage PD patients and 15 control subjects. The patients included in the study showed minimal motor abnormalities according to the Unified Parkinson's Disease Rating Scale (UPDRS).

    The symmetry indices were used to classify subjects into two different groups corresponding to PD or control. The proposed method presented high sensitivity and specificity with an area under the Receiver Operating Characteristic (ROC) curve of 0.872, 9\% greater than the second best method. The proposed method also showed an excellent Intraclass Correlation Coefficient (ICC) of 0.949, 55\% greater than the second best method. Results suggest that the proposed symmetry index is appropriate for this particular group of patients.

  • 24.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    A linguistic approach to the analysis of accelerometerdata for gait analysis2010In: Proceedings of the seventh IASTED International Conference on Biomedical Engineering: February 17-19, 2010, Innsbruck, Austria / [ed] A. Hierlemann, Anaheim, CA: ACTA Press, 2010, p. 8-Conference paper (Refereed)
    Abstract [en]

    There is evidence that many cognitive conditions affect the human motor system. Gait analysis has lately been used as a means of studying this physical-cognitive correlation. The development of gait analysis systems, able to record and analyze gait during normal daily activities and in uncontrolled environment, is an important addition to this area of research. Lately, linguistic approaches have been studied as means to achieve activity classification from vision sensors. The present work aims to extend the linguistic approach to achieve quantitative analysis of gait from accelerometer data. The proposed method can be used to extend the Human Activity Language framework to include the analysis of inertial sensors such as accelerometers. Results show that the proposed method is more accurate and robust than previous methods and can be used to extract a number of clinically relevant gait measurements. A novel symmetry index is presented to exemplify how the proposed method is able to extract more information from accelerometer signals than previous methods.

  • 25.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A Symbol-Based Approach to Gait Analysis From Acceleration Signals: Identification and Detection of Gait Events and a New Measure of Gait Symmetry2010In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 14, no 5, p. 1180-1187Article in journal (Refereed)
    Abstract [en]

    Gait analysis can convey important information about one’s physical and cognitive condition. Wearable inertial sensor systems can be used to continuously and unobtrusively assess gait during everyday activities in uncontrolled environments. An important step in the development of such systems is the processing and  analysis of the sensor data. This paper presents a symbol-based method used to detect the phases of gait and convey important dynamic information from accelerometer signals. The addition of expert knowledge substitutes the need for supervised learning techniques, rendering the system easy to interpret and easy to improve incrementally. The proposed method is compared to an approach based on peak-detection. A new symbol-based symmetry index is created and compared to a traditional temporal symmetry index and a symmetry measure based on cross-correlation. The symbol-based symmetry index exemplifies how the proposed method can extract more information from the acceleration signal than previous approaches

  • 26.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Developing a Motion Language: Gait Analysis from Accelerometer Sensor Systems2009In: Pervasive Health 2009: 3rd International Conference on Pervasive Computing Technologies for Healthcare, Piscataway, N.J.: IEEE Press, 2009, p. 1-8Conference paper (Refereed)
    Abstract [en]

    The advances in sensing technology provide us with the opportunity to develop mobile and unobtrusive systems to continuously gather gait data. Accelerometers have been shown to be an adequate choice for recording human motion data. For that reason, many previous works have investigated the use of accelerometers for gait analysis. Previous works were able to extract either static temporal information or dynamic general information about the gait patterns. This work aims at extracting both static and dynamic information from acceleration signals. The ability to extract information about the dynamics of gait is exemplified with a novel symmetry measure. The method presented here is based on the motion language approach. A method based on peak detection was chosen as a reference, which we compare to our method. A Gait Rite pressure sensitive mat was used to detect heel-strike and toe-off ground truths. Results show that the proposed approach is as accurate as, more robust than, and conveys more information than the reference method.

  • 27.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wickström, Nicholas
    Symbolic Approach to Motion Analysis: Framework and Gait Analysis Case Studies2013In: Telehealthcare Computing and Engineering: Principles and Design / [ed] Fei Hu, Boca Raton: CRC Press, 2013, 1, p. 561-606Chapter in book (Other academic)
  • 28.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Symbolization of time series: an evaluation of SAX, persist, and ACA2011In: CISP 2011: Proceedings, the 4th International Congress on Image and Signal Processing, 15-17 October 2011, Shanghai, China / [ed] Peihua Qiu, Piscataway, N.J.: IEEE Press, 2011, p. 2223-2228Conference paper (Refereed)
    Abstract [en]

    Symbolization of time-series has successfully been used to extract temporal patterns from experimental data. Segmentation is an unavoidable step of the symbolization process, and it may be characterized on two domains: the amplitude and the temporal domain. These two groups of methods present advantages and disadvantages each. Can their performance be estimated a priori based on signal characteristics? This paper evaluates the performance of SAX, Persist and ACA on 47 different time-series, based on signal periodicity. Results show that SAX tends to perform best on random signals whereas ACA may outperform the other methods on highly periodic signals. However, results do not support that a most adequate method may be determined a priory.

  • 29.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Eklund, Helene
    Center for Person-Centered Care, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Tranberg, Roy
    Department of Orthopedics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    A wearable gait analysis system using inertial sensors Part II: Evaluation in a clinical setting2012In: BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, [S. l.]: SciTePress, 2012, p. 5-14Conference paper (Refereed)
    Abstract [en]

    The gold standard for gait analysis, in-lab 3D motion capture, is not routinely used for clinical assessment due to limitations in availability, cost and required training. Inexpensive alternatives to quantitative gait analysis are needed to increase the its adoption. Inertial sensors such as accelerometers and gyroscopes are promising tools for the development of wearable gait analysis (WGA) systems. The present study evaluates the use of a WGA system on hip-arthroplasty patients in a real clinical setting. The system provides information about gait symmetry and normality. Results show that the normality measurements are well correlated with various quantitative and qualitative measures of recovery and health status.

  • 30.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Wickström, Nicholas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Eklund, Helene
    Sahlgrenska Academy, Göteborg, Sweden.
    Zügner, Roland
    Sahlgrenska Academy, Göteborg, Sweden.
    Tranberg, Roy
    Sahlgrenska Academy, Göteborg, Sweden.
    Assessment of Gait Symmetry and Gait Normality Using Inertial Sensors: In-Lab and In-Situ Evaluation2013In: Biomedical Engineering Systems and Technologies: 5th International Joint Conference, BIOSTEC 2012, Vilamoura, Portugal, February 1-4, 2012, Revised Selected Papers / [ed] Joaquim Gabriel et al., Heidelberg: Springer Berlin/Heidelberg, 2013, p. 239-254Chapter in book (Refereed)
    Abstract [en]

    Quantitative gait analysis is a powerful tool for the assessment of a number of physical and cognitive conditions. Unfortunately, the costs involved in providing in-lab 3D kinematic analysis to all patients is prohibitive. Inertial sensors such as accelerometers and gyroscopes may complement in-lab analysis by providing cheaper gait analysis systems that can be deployed anywhere. The present study investigates the use of inertial sensors to quantify gait symmetry and gait normality. The system was evaluated in-lab, against 3D kinematic measurements; and also in-situ, against clinical assessments of hip-replacement patients. Results show that the system not only correlates well with kinematic measurements but it also corroborates various quantitative and qualitative measures of recovery and health status of hip-replacement patients

  • 31.
    Sant'Anna, Anita
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Wickström, Nicholas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Zügner, Roland
    Department of Orthopedics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Tranberg, Roy
    Department of Orthopedics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    A wearable gait analysis system using inertial sensors Part I: Evaluation of measures of gait symmetry and normality against 3D kinematic data2012In: BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, [S. l.]: SciTePress, 2012, p. 180-188Conference paper (Refereed)
    Abstract [en]

    Gait analysis (GA) is an important tool in the assessment of several physical and cognitive conditions. The lack of simple and economically viable quantitative GA systems has hindered the routine clinical use of GA in many areas. As a result, patients may be receiving sub-optimal treatment. The present study introduces and evaluates measures of gait symmetry and gait normality calculated from inertial sensor data. These indices support the creation of mobile, cheap and easy to use quantitative GA systems. The proposed method was compared to measures of symmetry and normality derived from 3D kinematic data. Results show that the proposed method is well correlated to the kinematic analysis in both symmetry (r=0.84, p<0.0001) and normality (r=0.81, p<0.0001). In addition, the proposed indices can be used to classify normal from abnormal gait.

  • 32.
    Skärsäter, Ingela
    et al.
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Barkström, Magdalena
    Region Halland, Halmstad, Sweden.
    Bergman, Stefan
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Dahlqvist Jönsson, Patrik
    Region Halland, Halmstad, Sweden.
    Halila, Fawzi
    Halmstad University, School of Business, Engineering and Science, Centre for Innovation, Entrepreneurship and Learning Research (CIEL).
    Hertz, Anne-Christine
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Nygren, Jens
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Sjöberg, Jeanette
    Halmstad University, School of Education, Humanities and Social Science, Center for Social Analysis (CESAM).
    Tylenius, Andreas
    Theme Health Innovation at Halmstad University  - research, education and collaboration for welfare technology2015In: Abstracts: 19th International Philosophy of Nursing Society (IPONS) conference August 24-26, 2015 Karolinska Institutet, Stockholm, Sweden: Technology, Health Care and Person-centeredness: Beyond Utopia and Dystopia. Thinking the Future., Stockholm: Karolinska Institutet , 2015, p. 41-41Conference paper (Refereed)
    Abstract [en]

    In face of escalating health care costs, new technology holds great promise for innovative solutions and new more sustainable health care model. Welfare technology around a person allowing for greater autonomy and control in health issues and access to tailored information and personalized health behavior interventions. While this offers good opportunities for both public health impact, it also emphasizes the need for properly knowledge base and organizational structure to support a person- centred approach in the development of welfare technology in society. 

    Halmstad University initiated in 2014 a thematic research and educational initiative that has been named Theme Health Innovation. The initiative includes research, education and interaction with the community, region and industry, which in collaboration can contribute with innovative and sustainable solutions to social challenges in the health field. The starting point for the work is action based on societal and individual needs and development of venues for collaboration between different actors and levels of organization. 

    Theme Health Innovation aims to develop and affect people's ability to maintain and promote their health and prevent ill health. Health Innovations developed in encounters between different knowledge, skills and experiences, both within the university's research and education in collaboration with industry and the public sector. Health Innovations that are developed should be based on the needs from the people who will use the innovation, thus have an end user perspective. 

    At the conference, the Theme Health Innovation will be presented including the organizational structure, research as well as training in higher education that support the welfare technical development.

  • 33.
    Taha, Walid
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Duracz, Adam
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Zeng, Yingfu
    Rice University, Houston TX, USA.
    Atkinson, Kevin
    Rice University, Houston TX, USA.
    Bartha, Ferenc Ágoston
    Rice University, Houston TX, USA.
    Brauner, Paul
    Rice University, Houston TX, USA.
    Duracz, Jan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Xu, Fei
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Cartwright, Robert
    Rice University, Houston TX, USA.
    Konečný, Michal
    Computer Science Group, Aston University, Birmingham, United Kingdom.
    Moggi, Eugenio
    University of Genova, Genoa, Italy.
    Masood, Jawad
    Rice University, Houston TX, USA.
    Andreasson, Björn Pererik
    Halmstad University, School of Information Technology.
    Inoue, Jun
    Rice University, Houston TX, USA.
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Philippsen, Roland
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Chapoutot, Alexandre
    ENSTA ParisTech - U2IS, Paris, France.
    O'Malley, Marcia
    Department of Mechanical Engineering, Rice University, Houston TX, USA.
    Ames, Aaron
    School of Mechanical Eng., Georgia Institute of Technology, Atlanta GA, USA.
    Gaspes, Veronica
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Hvatum, Lise
    Schlumberger, Houston TX, USA.
    Mehta, Shyam
    Schlumberger, Houston TX, USA.
    Eriksson, Henrik
    Dependable Systems, SP Technical Research Institute of Sweden, Borås, Sweden.
    Grante, Christian
    AB Volvo, Gothenburg, Sweden.
    Acumen: An Open-source Testbed for Cyber-Physical Systems Research2016In: Internet of Things. IoT Infrastructures: Second International Summit, IoT 360° 2015, Rome, Italy, October 27-29, 2015. Revised Selected Papers, Part I / [ed] Benny Mandler, Johann Marquez-Barja, Miguel Elias Mitre Campista, Dagmar Cagáňová, Hakima Chaouchi, Sherali Zeadally, Mohamad Badra, Stefano Giordano, Maria Fazio, Andrey Somov & Radu-Laurentiu Vieriu, Heidelberg: Springer, 2016, Vol. 169, p. 118-130Conference paper (Refereed)
    Abstract [en]

    Developing Cyber-Physical Systems requires methods and tools to support simulation and verification of hybrid (both continuous and discrete) models. The Acumen modeling and simulation language is an open source testbed for exploring the design space of what rigorous-but-practical next-generation tools can deliver to developers of Cyber-Physical Systems. Like verification tools, a design goal for Acumen is to provide rigorous results. Like simulation tools, it aims to be intuitive, practical, and scalable. However, it is far from evident whether these two goals can be achieved simultaneously.

    This paper explains the primary design goals for Acumen, the core challenges that must be addressed in order to achieve these goals, the "agile research method" taken by the project, the steps taken to realize these goals, the key lessons learned, and the emerging language design. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.

  • 34.
    Taheri, Tayebeh
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Non-Invasive Breathing Rate Detection Using a Very Low Power Ultra-wide-band Radar2014In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) / [ed] Huiru (Jane) Zheng, Werner Dubitzky, Xiaohua Hu, Jin-Kao Hao, Daniel Berrar, Kwang-Hyun Cho, Yadong Wang & David Gilbert, Piscataway, NJ: IEEE Press, 2014, p. 78-83Conference paper (Refereed)
    Abstract [en]

    In this paper we present a novel method for remote breathing detection based on ultra-wide-band (UWB) radar. This is a method that does not require any wearable sensors, making it more comfortable and convenient for users. Furthermore, because of the wall penetrating characteristics of the transmitted signal, our system is useful in emergency situations such as monitoring people who may be trapped under earthquake rubble. For our investigation we used a Novelda UWB radar that provides high processing speed and low power consumption. In this paper we present two new convolution-based methods to extract breathing rate information from the received radar signal. This method was tested on several people who were monitored while laying down on a bed. The subject's position and breathing rate were calculated. Experimental results including 20 different subjects are provided, showing that this is a viable method for monitoring breathing rate using a low-power UWB radar.

1 - 34 of 34
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf