This paper presents the results of an interview‐based study of the use of virtual learning environments (VLEs) among dyslexic students. Interviews were carried out with 12 informants who had been formally diagnosed as dyslexic. The informants were either enrolled in a university or college programme, or had graduated less than a year before the interview. The findings reveal that dyslexic students experience a number of challenges associated with VLE use, including information overload, imperfect word processing tools, inadequate search functions, and having to relate to more than one system at a time.

In this paper, we present PMData: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person's weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes' performance. \datasetname combines input from Fitbit Versa 2 smartwatch wristbands, the PMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.

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A significant portion of the population have dyslexia, which is commonly associated with reading and writing difficulties. In the context of developing materials well-suited for users with reading disorders, one solution has been to develop materials especially targeted at dyslexic users. However, how are the attitudes among users with dyslexia towards adaptation? In this paper, we report the findings from qualitative interviews with 20 adults with dyslexia. The main finding was that they were sceptical towards adapted products, among others because it made them "feel stupid" and because the adapted format affected the reading experience negatively. In this paper we argue to instead work within the universal design paradigm, trying to develop products and services usable by all people, thus reducing the need for particular user groups to utilise "special solutions".

In recent years, the ultra-wideband (UWB) radar technology has shown great potential in monitoring activities of daily living (ADLs) for smart homes. In this paper, we investigate the significance of using non-wearable UWB sensors for developing non-intrusive, unobtrusive, and privacy-preserving monitoring of elderly ADLs. A controlled experiment was setup, implementing multiple non-wearable sensors in a smart home Lab setting. A total of nine (n = 9) participants were involved in conducting predefined scenarios of ADLs- cooking, eating, resting, sleeping and mobility. We employed the UWB sensing prototype and conventional implementation technologies, and the sensed data of both systems were stored, analysed and their performances were compared. The result shows that the performance of the non-wearable UWB technology is as good as that of the conventional ones. Furthermore, we provided a proof-of-concept solution for the real-time detection of abnormal behaviour based on excessive activity levels, and a model for automatic alerts to caregivers for timely medical assistance on-demand.