Context-awareness in Consumer IoT Technologies – CACT
||Carl Magnus Olsson
||Paul Davidsson, Fredrik Ohlin, Jeanette Eriksson and Nancy Russo
||Sony Mobile Communications, ÅF Technology
||The Knowledge Foundation and Businesspartners
||2014-12-01 -- 2017-11-30
||Internet of Things and People Research Centre
||Faculty of Technology and Society, [Missing text /mah/research/faculties/institutionenfordatavetenskapochmedieteknik for en]
Exploring how IoT-based context information can benefit meaningful designs for consumers.
The increasing number of consumer devices and services that track behavior have the potential to inform design of context-aware Internet of Things (IoT) technologies. This project is an exploratory industry-academia collaboration focusing on end-user value creation. Two research questions will be explored: One, how does rich use of context cues and context-specific services impact the design of novel and meaningful IoT-based services for individuals and groups? Two, in what ways can context-awareness contribute to appropriate design and use of system autonomy in IoT-based consumer services?
While additional cases may be defined over the project lifespan, two initial cases have been defined for addressing these research questions. The first case is based on the Sony Lifelog application as it exemplifies an ambitious state-of-practice lifelogging service that tracks contextual information from a broad range of situations, from sleep behavior to use of email. The analysis tools, however is primarily presenting the information as historical snippets which have largely been de-contextualized, making it difficult for users to see value beyond an initial curiosity towards what the application does.
The second case focuses on social entertainment services that recommend group activities based on analysis of individual group member’s interests, activities and preferences. The case will integrate existing Sony software components for social context analysis with advances in lifelogging and IoT-based activity-tracking devices to monitor the behavior that the recommended actions actually lead to (an aspect no previously reported study includes), and use the results to further improve future recommendations.