Supporting User Understanding and Engagement in Designing Intelligent Systems for the Home.

With advances in computing, networking and sensing technology, our everyday objects have become more automated, connected, and intelligent. This dissertation aims to inform the design and implementation of future intelligent systems and devices. To do so, this dissertation presents three studies that investigated user interaction with and experience of intelligent systems. In particular, we look at intelligent technologies that employ sensing technology and machine learning algorithm to perceive and respond to user behavior, and that support energy savings in the home. We first investigated how people understand and use an intelligent thermostat in their everyday homes to identify problems and challenges that users encounter. Subsequently, we examined the opportunities and challenges for intelligent systems that aimed to save energy, by comparing how people’s interaction changed between conventional and smart thermostats as well as how interaction with smart thermostats changed over time. These two qualitative studies led us to the third study. In the final study, we evaluated a smart thermostat that offered a new approach to the management of thermostat schedule in a field deployment, exploring effective ways to define roles for intelligent systems and their users in achieving their mutual goals of energy savings. Based on findings from these studies, this dissertation argues that supporting user understanding and user control of intelligent systems for the home is critical allowing users to intervene effectively when the system does not work as desired. In addition, sustaining user engagement with the system over time is essential for the system to obtain necessary user input and feedback that help improve the system performance and achieve user goals. Informed by findings and insights from the studies, we identify design challenges and strategies in designing end-user interaction with intelligent technologies for the home: making system behaviors intuitive and intelligible; maintaining long-term, easy user engagement over time; and balancing interplay between user control and system autonomy to better achieve their mutual goals.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133318/1/rayang_1.pd

“No powers, man!”: A student perspective on designing university smart building interactions

Smart buildings offer an opportunity for better performance and enhanced experience by contextualising services and interactions to the needs and practices of occupants. Yet, this vision is limited by established approaches to building management, delivered top-down through professional facilities management teams, opening up an interaction-gap between occupants and the spaces they inhabit. To address the challenge of how smart buildings might be more inclusively managed, we present the results of a qualitative study with student occupants of a smart building, with design workshops including building walks and speculative futuring. We develop new understandings of how student occupants conceptualise and evaluate spaces as they experience them, and of how building management practices might evolve with new sociotechnical systems that better leverage occupant agency. Our findings point to important directions for HCI research in this nascent area, including the need for HBI (Human-Building Interaction) design to challenge entrenched roles in building management

Digital energy visualisations in the workplace: the e-Genie tool

Building management systems are designed for energy managers; there are few energy feedback systems designed to engage staff. A tool, known as e-Genie, was developed to engage workplace occupants with energy data and support them to take action to reduce energy use. Building on research insights within the field, e-Genie’s novel approach encourages users to make plans to meet energy saving goals, supports discussion, and considers social energy behaviours (e.g. discussing energy issues, taking part in campaigns) as well as individual actions. A field based study of e-Genie indicated that visualisations of energy data were engaging and that the discussion ‘Pinboard’ was particularly popular. Pre- and post survey (N = 77) evaluation of users indicated that people were significantly more concerned about energy issues and reported engaging more in social energy behaviour after ~two weeks of e-Genie being installed. Concurrently, objective measures of electricity use decreased over the same period, and continued decreasing over subsequent weeks. Indications are that occupant facing energy feedback visualisations can be successful in reducing energy use in the workplace; furthermore supporting social energy behaviour in the workplace is likely to be a useful direction for promoting action

A Design Exploration of Health-Related Community Displays

The global population is ageing, leading to shifts in healthcare needs. It is well established that increased physical activity can improve the health and wellbeing of many older adults. However, motivation remains a prime concern. We report findings from a series of focus groups where we explored the concept of using community displays to promote physical activity to a local neighborhood. In doing so, we contribute both an understanding of the design space for community displays, as well as a discussion of the implications of our work for the broader CSCW community. We conclude that our work demonstrates the potential for developing community displays for increasing physical activity amongst older adults

Evaluating the effect of feedback from different computer vision processing stages: a comparative lab study

Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applications targeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our findings indicate that the stage of processing from which feedback is derived plays an important role in users' ability to develop coherent and correct understandings of a system's operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms

Dataset: Evaluating the Effect of Feedback from Different Computer Vision Processing Stages

Dataset supports: J. Kittley-Davies, A. Alqaraawi, R. Yang, E. Costanza, A. Rogers, and S. Stein. 2019. Evaluating the Effect of Feedback from Different Computer Vision Processing Stages: A Comparative Lab Study. In CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4&ndash;9, 2019, Glasgow, Scotland UK. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3290605.3300273</span

Guess the Data: Data Work to Understand How People Make Sense of and Use Simple Sensor Data from Homes

Simple smart home sensors, e.g. for temperature or light, increasingly collect seemingly inconspicuous data. Prior work has shown that human sensemaking of such sensor data can reveal domestic activities. Such sensemaking presents an opportunity to empower people to understand the implications of simple smart home sensors. To investigate, we developed and field-tested the Guess the Data method, which enabled people to use and make sense of live data from their homes and to collectively interpret and reflect on anonymized data from the homes in our study. Our findings show how participants reconstruct behavior, both individually and collectively, expose the sensitive personal data of others, and use sensor data as evidence and for lateral surveillance within the household. We discuss the potential of our method as a participatory HCI method for investigating design of the IoT and implications created by doing data work on home sensors.</p