2 research outputs found
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Privacy-aware Smart Home Interface Framework
Smart home user interfaces are pervasive and shared by multiple users who occupy the space. Therefore, they pose a risk to interpersonal privacy of occupants because an individual’s sensitive information can be leaked to other co-occupants (information privacy), or they can be disturbed by intrusions into their personal space (physical privacy) when the co-occupant interacts with the smart home user interfaces. This thesis hypothesises that interpersonal privacy violations can be mitigated by adapting the user interface layer and presents insights into how to achieve usable user interface adaptation to mitigate or minimise interpersonal privacy violations in smart homes.
The thesis reports two case studies and two user studies. The first case study identifies the key characteristics needed to model the rich context of interpersonal privacy violations scenarios. Then it presents knowledge representation models that are required to represent the identified characteristics and evaluates them for adequacy in modelling the context information of interpersonal privacy violation scenarios. The second case study presents a software architecture and a set of algorithms that can detect interpersonal privacy violations and generate usable user interface adaptations. Then it evaluates the architecture and the algorithms for adequacy in generating usable privacy-aware user interface adaptations. The first user study (N=15) evaluates the usability of the adaptive user interfaces generated from the framework where storyboards were used as the stimulant. Extending the findings from the usability study and expanding the coverage of example scenarios, the second user study (N=23) evaluates the overall user experience of the adaptive user interfaces, using video prototypes as the stimulant.
The research demonstrates that the characteristics identified, and the respective knowledge representation models adequately captured the context of interpersonal privacy violation scenarios. Furthermore, the software architecture and the algorithms could detect possible interpersonal privacy violations and generate usable user interface adaptations to mitigate them. The two user studies demonstrate that the adaptive user interfaces, when used in appropriate situations, were a suitable solution for addressing interpersonal privacy violations while providing high usability and a positive user experience. The thesis concludes by providing recommendations for developing privacy-aware user interface adaptations and suggesting future work that can extend this research
Loneliness in older people and COVID-19: Applying the social identity approach to digital intervention design
The COVID-19 pandemic is worsening loneliness for many older people through the challenges it poses in engaging with their social worlds. Digital technology has been offered as a potential aid, however, many popular digital tools have not been designed to address the needs of older adults during times of limited contact. We propose that the Social Identity Model of Identity Change (SIMIC) could be a foundation for digital loneliness interventions. While SIMIC is a well-established approach for maintaining wellbeing during life transitions, it has not been rigorously applied to digital interventions. There are known challenges to integrating psychological theory in the design of digital technology to enable efficacy, technology acceptance, and continued use. The
interdisciplinary field of Human Computer Interaction has a history of drawing on models originating from psychology to improve the design of digital technology and to design technologies in an appropriate manner. Drawing on key lessons from this literature, we consolidate research and design guidelines for multidisciplinary research applying psychological theory such as SIMIC to digital social interventions for loneliness