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  • 1.
    Hagelbäck, Johan
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Liapota, Pavlo
    Softwerk AB.
    Lincke, Alisa
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Löwe, Welf
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Variants of Dynamic Time Warping and their Performance in Human Movement Assessment2019In: 21st International Conference on Artificial Intelligence (ICAI'19: July 29 - August 1, 2019, las Vegas, USA), CSREA Press, 2019Conference paper (Refereed)
    Abstract [en]

    The advent of commodity 3D sensor technology enabled, amongst other things, the efficient and effective assessment of human movements. Statistical and machine learning approaches map recorded movement instances to expert scores to train models for the automated assessment of new movements. However, there are many variations in selecting the approaches and setting the parameters for achieving good performance, i.e., high scoring accuracy and low response time. The present paper researches the design space and the impact of sequence alignment on accuracy and response time. More specifically, we introduce variants of Dynamic Time Warping (DTW) for aligning the phases of slow and fast movement instances and assess their effect on the scoring accuracy and response time. Results show that an automated stripping of leading and trailing frames not belonging to the movement (using one DTW variant) followed by an alignment of selected frames in the movements (based on another DTW variant) outperforms the original DTW and other suggested variants thereof. Since these results are independent of the selected learning approach and do not rely on the movement specifics, the results can help improving the performance of automated human movement assessment, in general.

  • 2.
    Hagelbäck, Johan
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lincke, Alisa
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Löwe, Welf
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Rall, Eduard
    AIMO AB.
    On the Agreement of Commodity 3D Cameras2019In: 23rd International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'19: July 29 - August 1, 2019, USA), CSREA Press, 2019Conference paper (Refereed)
    Abstract [en]

    The advent of commodity 3D sensor technol- ogy has, amongst other things, enabled the efficient and effective assessment of human movements. Machine learning approaches do not rely manual definitions of gold standards for each new movement. However, to train models for the automated assessments of a new movement they still need a lot of data that map recorded movements to expert judg- ments. As camera technology changes, this training needs to be repeated if a new camera does not agree with the old one. The present paper presents an inexpensive method to check the agreement of cameras, which, in turn, would allow for a safe reuse of trained models regardless of the cameras. We apply the method to the Kinect, Astra Mini, and Real Sense cameras. The results show that these cameras do not agree and that the models cannot be reused without an unacceptable decay in accuracy. However, the suggested method works independent of movements and cameras and could potentially save effort when integrating new cameras in an existing assessment environment.

  • 3.
    Herault, Romain Christian
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lincke, Alisa
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Forsgärde, Elin-Sofie
    Linnaeus University, Faculty of Health and Life Sciences, Department of Health and Caring Sciences.
    Elmqvist, Carina
    Linnaeus University, Faculty of Health and Life Sciences, Department of Health and Caring Sciences.
    Using 360-degrees interactive videos inpatient trauma treatment education: design, development and evaluationaspects2018In: Smart Learning Environments, E-ISSN 2196-7091, Vol. 5, article id 26Article in journal (Refereed)
    Abstract [en]

    Extremely catastrophic situations are rare in Sweden, which makes training opportunities important to ensure competence among emergency personnel who should be actively involved during such situations. There is a requirement to conceptualize, design, and implement an interactive learning environment that allows the education, training and assessment of these catastrophic situations more often, and in different environments, conditions and places. Therefore, to address these challenges, a prototype system has been designed and developed, containing immersive, interactive 360-degrees videos that are available via a web browser. The content of these videos includes situations such as simulated learning scenes of a trauma team working at the hospital emergency department. Various forms of interactive mechanisms are integrated within the videos, to which learners should respond and act upon. The prototype was tested during the fall term of 2017 with 17 students (working in groups), from a specialist nursing program, and four experts. The video recordings of these study sessions were analyzed and the outcomes are presented in this paper. Different group interaction patterns with the proposed tool were identified. Furthermore, new requirements for refining the 360-degrees interactive video, and the technical challenges associated with the production of this content, have been found during the study. The results of our evaluation indicate that the system can provide the students with novel interaction mechanisms, to improve their skills, and it can be used as a complementary tool for the teaching and learning methods currently used in their education process.

  • 4.
    Herault, Romain Christian
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lincke, Alisa
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Forsgärde, Elin-Sofie
    Linnaeus University, Faculty of Health and Life Sciences, Department of Health and Caring Sciences.
    Elmqvist, Carina
    Linnaeus University, Faculty of Health and Life Sciences, Department of Health and Caring Sciences.
    Svensson, Anders
    Linnaeus University, Faculty of Health and Life Sciences, Department of Health and Caring Sciences.
    Design and Evaluation of a 360 Degrees Interactive Video System to Support Collaborative Training for Nursing Students in Patient Trauma Treatment2018In: 26TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION (ICCE 2018) / [ed] Yang, JC Chang, M Wong, LH Rodrigo, MMT, Asia-Pacific Society for Computers in Education, 2018, p. 298-303Conference paper (Refereed)
    Abstract [en]

    Extreme catastrophe situations are rare in Sweden, which makes training opportunities important to secure the competence among emergency personnel that should be actively involved during those situations. There is a need to conceptualize, design and implement interactive learning environments that allow to educate, train and assess these catastrophe situations more often and in different settings, conditions and places. In order to address these challenges, a prototype system has been designed and developed containing immersive interactive 360 degrees educational videos that are available via a web browser. The content of these videos includes simulated learning scenes of a trauma team working at the hospital emergency department. Different types of interaction mechanisms are integrated within the videos in which learners should act upon and respond. The prototype was tested during the fall term 2017 with 17 students from the specialist nursing program, and four medical experts. These activities were assessed in order to get new insights into issues related to the proposed approach and feedback connected to the usefulness, usability and learnability of the suggested prototype. The initial outcomes of the evaluation indicate that the system can provide students with novel interaction mechanisms to improve their skills and it can be applied as a complementary tool to the methods used currently in their education.

  • 5.
    Lincke, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lozano Prieto, David
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Herault, Romain Christian
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Forsgärde, Elin-Sofie
    Linnaeus University, Faculty of Health and Life Sciences, Department of Health and Caring Sciences.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Visualizing learners’ navigation behaviour using 360 degrees interactive videos2019In: Proceedings of the 15th International Conference on Web Information Systems and Technologies / [ed] Alessandro Bozzon, Francisco Domínguez Mayo & Joaquim Filipe, Vienna: SciTePress, 2019, Vol. 1, p. 358-364Conference paper (Refereed)
    Abstract [en]

    The use of 360-degrees interactive videos for educational purposes in the medical field has increased in recent years, as well as the use of virtual reality in general. Learner’s navigation behavior in 360-degrees interactive video learning environments has not been thoroughly explored yet. In this paper, a dataset of interactions generated by 80 students working in 16 groups while learning about patient trauma treatment using 360-degrees interactive videos is used to visualize learners’ navigation behavior. Three visualization approaches were designed and implemented for exploring users’ navigation paths and patterns of interaction with the learning materials are presented and discussed. The visualization tool was developed to explore the issues above and it provides a comprehensive overview of the navigation paths and patterns. A user study with four experts in the information visualization field has revealed the advantages and drawbacks of our solution. The paper concludes by providing some suggestions for improvements of the proposed visualizations.

  • 6.
    Lincke, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lundberg, Jenny
    Thunander, Maria
    Lund University .
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Lundberg, Jonas
    Jusufi, Ilir
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Diabetes Information in Social Media2018In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18) / [ed] Karsten Klein, Yi-Na Li, and Andreas Kerren, ACM Publications, 2018, p. 104-105Conference paper (Refereed)
    Abstract [en]

    Social media platforms have created new ways for people to communicate and express themselves. Thus, it is important to explore how e-health related information is generated and disseminated in these platforms. The aim of our current efforts is to investigate the content and flow of information when people in Sweden use Twitter to talk about diabetes related issues. To achieve our goals, we have used data mining and visualization techniques in order to explore, analyze and cluster Twitter data we have collected during a period of 10 months. Our initial results indicate that patients use Twitter to share diabetes related information and to communicate about their disease as an alternative way that complements the traditional channels used by health care professionals.

  • 7.
    Sotsenko, Alisa
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    A Rich Context Model: Design and Implementation2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The latest developments of mobile devices include a variety of hardware features that allow for more rich data collection and services. Numerous sensors, Internet connectivity, low energy Bluetooth connectivity to other devices (e.g., smart watches, activity tracker, health data monitoring devices) are just some examples of hardware that helps to provide additional information that can be beneficially used for many application domains. Among others, they could be utilized in mobile learning scenarios (for data collection in science education, field trips), in mobile health scenarios (for health data collection and monitoring the health state of patients, changes in health conditions and/or detection of emergency situations), and in personalized recommender systems. This information captures the current context situation of the user that could help to make mobile applications more personalized and deliver a better user experience. Moreover, the context related information collected by the mobile device and the different applications can be enriched by using additional external information sources (e.g., Web Service APIs), which help to describe the user’s context situation in more details.

    The main challenge in context modeling is the lack of generalization at the core of the model, as most of the existing context models depend on particular application domains or scenarios. We tackle this challenge by conceptualizing and designing a rich generic context model. In this thesis, we present the state of the art of recent approaches used for context modeling and introduce a rich context model as an approach for modeling context in a domain-independent way. Additionally, we investigate whether context information can enhance existing mobile applications by making them sensible to the user’s current situation. We demonstrate the reusability and flexibility of the rich context model in a several case studies. The main contributions of this thesis are: (1) an overview of recent, existing research in context modeling for different application domains; (2) a theoretical foundation of the proposed approach for modeling context in a domain-independent way; (3) several case studies in different mobile application domains.

  • 8.
    Sotsenko, Alisa
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Exploring Different Use Cases for a Rich Context Model for Mobile Applications2015In: Proceedings of Doctoral Symposium of the 9th International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT 2015) / [ed] Peter Werner Eklund & Rebekeh Wegener, 2015, Vol. 1537, p. 23-31Conference paper (Refereed)
    Abstract [en]

     Substantial research in the field of context modeling has explored aspects related to the use of contextualization in various mobile scenarios. The current context of a mobile user has been often limited to his/her current position, neglecting the possibilities offered by modern mobile devices of providing a much richer representation of the current user’s context. This research aims to improve the usability of users’ context in the mobile software development process. Therefore, this paper presents the proposal of a rich context model (RCM) as general approach for context modeling to explore the context of the users in different application domains.

  • 9.
    Sotsenko, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Jansen, Marc
    University of Applied Sciences, Ruhr West Bottrop, Germany.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    About the Contextualization of Learning Objects in Mobile Learning Settings2013In: QScience Proceedings: Vol. 2013, 12th World Conference on Mobile and Contextual Learning (mLearn 2013), Qatar: QScience.com , 2013, p. 67-70Conference paper (Refereed)
    Abstract [en]

    In recent years, many efforts have been undertaken in order to design and deploy learning activities that make use ofmodern mobile devices, like smartphones and tablet PC’s. Hence, new possibilities for supporting these so-called mobilelearning scenarios have risen. One of the major benefits of these kinds of learning scenarios is the possibility of a learnerto have access to learning content independent of time and place and therefore, enabling learners to learn in very differentsituations. In order to support learning across different settings, this paper discusses an approach that allows identifying abest fitting format of a Learning Object (LO) with respect to the current situation of the learner. This approach allows todelivering learning content in a format that may suit the current context of the learner and therefore, it enables seamlesslearning.

  • 10.
    Sotsenko, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Jansen, Marc
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology. University of Applied Sciences Ruhr Wes, Germany.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Implementing and Validating a Mobile Learning Scenario Using Contextualized Learning Objects2014In: Proceedings of the 22nd International Conference on Computers in Education ICCE 2014: November 30, 2014 - December 4, 2014, Nara, Japan, Japan: Asia-Pacific Society for Computers in Education, 2014, p. 522-527Conference paper (Refereed)
    Abstract [en]

    Substantial research in the field of mobile learning has explored aspects related to contextualized learning scenarios. Nevertheless, the current context of a mobile learner has been often limited to his/her current position, neglecting the possibilities offered by modern mobile devices of providing a much richer representation of the current learner´s context. In this paper, we show that a detailed contextualization of the learner may provide benefits in mobile learning scenarios. In order to validate this claim, we implemented a mobile learning scenario based on an approach that allows for a very rich and detailed contextualization of the mobile learner. The scenario that we implemented allowed exchange students to be guided at Linnaeus University in Växjö, Sweden in order to get familiar with the campus and prominent institutions on it. We carried out a study including two groups; one that performed learning activities with contextualization support and one other without it. The results of our evaluation showed significantly better results for the contextualized approach, especially with respect to the acceptance of the Perceived Ease of Use.

  • 11.
    Sotsenko, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Jansen, Marc
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology. University of Applied Sciences Ruhr West. Germany.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Supporting Content Contextualization in Web Based Applications on Mobile Devices2013In: Proceedings of the 9th International Conference on Web Information Systems and Technologies: Aachen, Germany, 8-10 May, 2013 / [ed] Karl-Heinz Krempels, Alexander Stocker, SciTePress, 2013, p. 501-504Conference paper (Refereed)
    Abstract [en]

    Mobile devices, in the form of smartphones, are endowed with rich capabilities in terms of multimedia, sensors and connectivity. The wide adoption of these devices allows using them across different settings and situations. One area in which mobile devices become more and more prominent is within the field of mobile learning. Here, mobile devices provide rich possibilities for the contextualization of the learner, by using the set of sensors available in the device. On the one hand, the usage of mobile devices enables participation in learning activities independent of time and space. Nevertheless, developing mobile learning applications for the heterogeneity of mobile devices available in the market becomes a challenge. Not only this is a problem related to form factor aspects, but also the large number of different operating systems, platforms and app infrastructures (app stores) are aspects to be considered. In this paper we present our initial efforts with regard to the development of cross-platform mobile applications to support the contextualization of learning content. 

  • 12.
    Sotsenko, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Jansen, Marc
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Using a Rich Context Model for a News Recommender System for Mobile Users2014In: UMAP 2014 Extended Proceedings: Posters, Demos, Late-breaking Results and Workshop Proceedings of the 22nd Conference on User Modeling, Adaptation, and Personalization co-located with the 22nd Conference on User Modeling, Adaptation, and Personalization (UMAP2014) Aalborg, Denmark, July 7-11, 2014. / [ed] Iván Cantador, Min Chi, Rosta Farzan, Robert Jäschke, CEUR , 2014, Vol. 1181, p. 13-16Conference paper (Refereed)
    Abstract [en]

    Recommender systems have become an important application domain related to the development of personalized mobile services. Thus, various recommender mechanisms have been developed for filtering and delivering relevant information to mobile users. This paper presents a rich context model to provide the relevant content of news to the current context of mobile users. The proposed rich context model allows not only providing relevant news with respect to the user’s current context but, at the same time, also determines a convenient representation format of news suitable for mobile devices.

  • 13.
    Sotsenko, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Jansen, Marc
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Using a Rich Context Model for People-to-People Recommendation2015In: 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), 24-26 Aug. 2015, Rome, IEEE, 2015, p. 703-708Conference paper (Refereed)
    Abstract [en]

    In this paper we present an approach for People- to-People recommendations based on a Rich Context Model (RCM). We consider personal user information as contextual information used for our recommendations. The evaluation of our recommendation approach was performed on a social network of students. The obtained results do show a significant increase in performance while, at the same time, a slight increase in quality in comparison to a manual matching process. The proposed approach is flexible enough to handle different data types of contextual information and easy adaptable to other recommendation domains. 

  • 14.
    Sotsenko, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Jansen, Marc
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Rana, Juwel
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology. Telenor Grp, Norway.
    Using a Rich Context Model for Real-Time Big Data Analytics in Twitter2016In: 2016 IEEE 4TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD WORKSHOPS (FICLOUDW), IEEE, 2016, p. 228-233Conference paper (Refereed)
    Abstract [en]

    In this paper we present an approach for contextual big data analytics in social networks, particularly in Twitter. The combination of a Rich Context Model (RCM) with machine learning is used in order to improve the quality of the data mining techniques. We propose the algorithm and architecture of our approach for real-time contextual analysis of tweets. The proposed approach can be used to enrich and empower the predictive analytics or to provide relevant context-aware recommendations.

  • 15.
    Sotsenko, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Zbick, Janosch
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Jansen, Marc
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Contextualization of Mobile Learners2015In: Mobile Learning: Trends, Attitudes and Effectiveness / [ed] Mohamed Hamada, Nova Science Publishers, Inc., 2015, p. 39-54Chapter in book (Refereed)
    Abstract [en]

    This chapter describes our current research efforts related to the contextualization of learners in mobile learning activities. Substantial research in the field of mobile learning has explored aspects related to contextualized learning scenarios. However, new ways of interpretation and consideration of contextual information of mobile learners are necessary. This chapter provides an overview regarding the state of the art of innovative approaches for supporting contextualization in mobile learning. Additionally, we provide the description of the design and implementation of a flexible multi-dimensional vector space model to organize and process contextual data together with visualization tools for further analysis and interpretation. We also present a study with outcomes and insights on the usage of the contextualization support for mobile learners. To conlcude, we discuss the benefits of using contextualization models for learners in different use-cases. Moreover, a description is presented in order to illustrate how the proposed contextual model can easily be adapted and reused for different use-cases in mobile learning scenarios and potentially other mobile fields.

  • 16.
    Sotsenko, Alisa
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Zbick, Janosch
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Jansen, Marc
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology. University of Applied Sciences Ruhr West, Germany.
    Milrad, Marcelo
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology.
    Flexible and Contextualized Cloud Applications for Mobile Learning Scenarios2016In: Mobile, Ubiquitous, and Pervasive Learning: Fundaments, Applications, and Trends / [ed] Alejandro Peña-Ayala, Springer Publishing Company, 2016, p. 167-192Chapter in book (Refereed)
    Abstract [en]

    This chapter describes our research efforts related to the design of mobile learning (m-learning) applications in cloud-computing (CC) environments. Many cloud-based services can be used/integrated in m-learning scenarios, hence, there is a rich source of applications that could easily be applied to design and deploy those within the context of cloud-based services. Here, we present two cloud-based approaches—a flexible framework for an easy generation and deployment of mobile learning applications for teachers, and a flexible contextualization service to support personalized learning environment for mobile learners. The framework provides a flexible approach that supports teachers in designing mobile applications and automatically deploys those in order to allow teachers to create their own m-learning activities supported by mobile devices. The contextualization service is proposed to improve the content delivery of learning objects (LOs). This service allows adapting the learning content and the mobile user interface (UI) to the current context of the user. Together, this leads to a powerful and flexible framework for the provisioning of potentially ad hoc mobile learning scenarios. We provide a description of the design and implementation of two proposed cloud-based approaches together with scenario examples. Furthermore, we discuss the benefits of using flexible and contextualized cloud applications in mobile learning scenarios. Hereby, we contribute to this growing field of research by exploring new ways for designing and using flexible and contextualized cloud-based applications that support m-learning.

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