Grua, Eoin Martino

English

With the current digitisation of our world, we have witnessed a surge in the presence and use of mobile devices. Consequently, there has been a natural increase in the use of mobile applications (apps). A category of app that has been growing in popularity is e-Health apps. However, even though popular, e-Health apps have many shortcomings that need to be addressed. Most notably, the rules and mechanisms employed by current day e-Health apps do not use the full potential of context and features they have access to. Leading to apps that are too rigid and not well tailored to the users' needs and goals. In this thesis we look at overcoming this rigidity and sub-optimal tailoring of e-Health apps. To reach this goal, we propose combining AI-based personalisation and software self-adaptation. For personalisation, we choose to use reinforcement learning (RL) as it is a good fit in providing personalisation in the e-Health domain. We explore the current state of the art by conducting a systematic literature review. With this review we identify two main weaknesses of RL: it requires a lot of data to reach an optimal policy and exploration can lead to user disengagement. To tackle the former, we propose cluster-based RL. We then further improve our proposed solution by developing an online clustering algorithm designed for e-Health. For the latter, we explore how machine learning can be used to predict user engagement. To better understand software self-adaptation in the domain of apps, we conduct a systematic literature review. In the review, we classify the current approaches and identify several shortcomings relevant to e-Health apps. Lastly, to tackle the identified shortcomings and combine personalisation and self-adaptation, we introduce a reference architecture for personalised and self-adaptive e-Health apps. We explore the benefits that said architecture can have on social sustainability and empirically evaluate an app implemented following this architecture. For the empirical evaluation two experiments were performed: a user study and a measurement-based experiment. With the user study, we better understand the effects of the implemented app on the end users' perception and usability. With the measurement-based experiment, we investigate the effects that the app has on performance and energy consumption. Our results are promising, as the user study shows improved end users' usability and no significant drawback in end users' perception as well as no perceivable increase in energy consumption or decrease in performance

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The Future of E-Health is Mobile:Combining AI and Self-Adaptation to Create Adaptive E-Health Mobile Applications

https://research.vu.nl/files/139661964/E M  Grua - thesis.pdf

The Future of E-Health is Mobile:Combining AI and Self-Adaptation to Create Adaptive E-Health Mobile Applications

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