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  • 1.
    Hasanov, Aziz
    et al.
    Ajou University.
    Laine, Teemu
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Chung, Tae-Sun
    Ajou University.
    A survey of adaptive context-aware learning environments2019Inngår i: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 11, nr 5, s. 403-428Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    Adaptive context-aware learning environments (ACALEs) can detect the learner’s context and adapt learning materi-als to match the context. The support for context-awareness and adaptation is essential in these systems so that they can makelearning contextually relevant. Previously, several related surveys have been conducted, but they are either outdated or they donot consider the important aspects of context-awareness, adaptation and pedagogy in the domain of ACALEs. To alleviate this,a comprehensive literature search on ACALEs was first performed. After filtering the results, 53 studies that were publishedbetween 2010 and 2018 were analyzed. The highlights of the results are: (i) mobile devices (PDAs, mobile phones, smartphones)are the most common client types, (ii) RFID/NFC are the most common sensors, (iii) ontology is the most common context mod-eling approach, (iv) context data typically originates from the learner profile or the learner’s location, (v) rule-based adaptationis the most used adaptation mechanism, and (vi) informative feedback is the most common feedback type. Additionally, we con-ducted a trend analysis on technology usage in ACALEs throughout the covered timespan, and proposed a taxonomy of contextcategories as well as several other taxonomies for describing various aspects of ACALEs. Finally, based on the survey results,directions for future research in the field were given. These results can be of interest to educational technology researchers andto developers of adaptive and context-aware applications.

  • 2.
    Seo, Jungryul
    et al.
    Ajou University.
    Laine, Teemu H.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Accurate position and orientation independent step counting algorithm for smartphones2018Inngår i: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 10, nr 6, s. 481-495Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Step counting (SC) algorithms can be applied to different areas such as well-being applications, games, and indoor navigation. Many existing SC algorithms for smartphones use data from inertial sensors to infer the number of steps taken, but their usefulness in real-life situations is limited since typically only a few positions and orientations are supported. Moreover, the algorithms may suffer from dynamic orientation and position changes during walking. To alleviate these shortcomings, we propose the Position and Orientation Independent Step Counting Algorithm (POISCA), which uses an accelerometer and a gyroscope to count the number of steps while allowing the smartphone’s position and orientation to change dynamically. In a nutshell, the algorithm first determines the orientation of the smartphone, and then detects zero crossings with a predetermined buffer range. 48 young adults (36 males, 12 females) participated in an experiment that simulated a real-life scenario to evaluate the performance of POISCA against three other step counting algorithms. The data from 24 participants were randomly assigned to a training group, which was then used to establish threshold parameters for POISCA. The remaining 24 participants’ data were used for accuracy measurement. The results show that POISCA outperforms the other algorithms with a Symmetric Mean Absolute Percentage Error of 4.54%, which can be lower if the algorithm is calibrated for each user. The results suggest that POISCA has potential for use in real-life situations where changes in position and orientation of the smartphone are dynamic.

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