With population increases, global economic growth, and shifts in climate, the world is facing an unprecedented demand for resources that are becomingly increasingly scarce. Although often overlooked, our everyday activities such as commuting to work, showering, and clothes washing can have significant impact on the environment. The central problem addressed in this dissertation is not that humans negatively impact the environment—indeed, some amount of impact is unavoidable—but rather that we have insufficient means to monitor and understand this impact and to help change our behavior if we so desire. This dissertation focuses on creating new types of sensors to monitor and infer everyday human activity such as driving to work or taking a shower and taking this sensed information and feeding it back to the user in novel, engaging, and informative ways with the goal of increasing awareness and promoting environmentally responsible behavior. We refer to these sensing and feedback systems as eco-feedback technology. Our research takes advantage of a number of technology trends including the increasingly low cost of fast computation, advances in machine learning, and the prevalence and affordability of new types of display mediums (e.g., mobile phones) to design systems never before possible.
This dissertation provides a theoretical perspective with which to guide the design of new eco-feedback systems as well as specific formative and technical contributions for eco-feedback in the domains of personal transportation any water usage. Key contributions include the invention of new low-cost sensing systems for monitoring and inferring transit routines and disaggregated water usage in the home along with eco-feedback visualizations that take advantage of this unprecedented data. The approaches, empirical findings and a design space for eco-feedback should be of interest to researchers working on eco-feedback in HCI, Ubicomp and environmental psychology, as well as to practitioners and designers tasked with constructing new types of ecofeedback systems and/or utility bills. More broadly, this dissertation also has implications for the construction of sensing and feedback technology in general, including domains such as persuasive technology, personal informatics, and health behavior change.
|Advisor:||Landay, James A., Patel, Shwetak N.|
|School:||University of Washington|
|School Location:||United States -- Washington|
|Source:||DAI-B 73/07(E), Dissertation Abstracts International|
|Subjects:||Sustainability, Environmental science, Computer science|
|Keywords:||Ecofeedback, Personal transportation, Residential water usage|
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