Personal Informatics Systems and the Integration of Data from Novel Sensor Technologies

Student thesis: Doctoral ThesisPhD

Abstract

Personal Informatics (PI) systems are used by people to record, track and analyse data about themselves. The increasing availability and affordability of sensing technologies, which is likely to continue into the future, provides people with the ability to record and analyse an ever increasing amount of data about themselves. Primarily, research relating to Personal Informatics seeks to understand how people are using currently available, widely adopted sensing technologies, or how specific, mainstream sensing technologies might be used to solve a specific problem. Currently, there is little understanding of how people attempt to make sense of data from newly emerging sensing technologies; those which do not have widespread adoption, or whose data is less understood by the general population. Given the rapid expansion of sensing capabilities, the research described in this thesis provides an important understanding of how people use emerging sensing technologies to learn more about themselves, as well as the challenges and opportunities these technology present in relation to self-understanding. Throughout our research we used a NeuroSky brain-computer interface as a technology probe, representing a novel sensing technology, and developed a feature-rich multifaceted PI system for allowing users to engage with data. Our findings stem from qualitative analysis of participant interviews in one exploratory study and two in-the-wild studies of these novel sensing technologies. Additionally, we explore aspects of trust in novel sensing technologies. We present quantitative and qualitative analysis of participant responses from a lab-based study designed to explore how perceptions of trust are shaped. We present several design considerations and challenges for developing PI systems that integrate novel sensing technologies. These considerations and challenges are based around two groups of users with distinct tracking behaviours: those tracking for documentary purposes - to gather data that documents their lives - and those with a specific life-aspect that motivates data tracking, such as those living with chronic health conditions or symptoms. Specifically, we explore the impact of novel sensing technologies for those with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), prolonged fatigue, chronic fatigue (CF), and idiopathic chronic fatigue (ICF).
We additionally present four design considerations for the development of features which enable users to interrogate their data, using automatically generated insights and predefined analyses. We highlight that trust in devices may be shaped by users' preconceived initial levels of trust in a device, rather than on specific feedback from the device or visualisations of the data that it generates.
Date of Award8 Sep 2021
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorSimon Jones (Supervisor) & Eamonn O'Neill (Supervisor)

Keywords

  • personal informatics
  • quantified self
  • human-computer interaction
  • eHealth

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