Ph.D. student interested in Human-Computer Interaction
Hello! I am Nina Sakhnini. I am a Computer Engineer, a researcher and a Computer Science Ph.D. student at the University of Illinois at Chicago. My research interest is in Human-Computer Interaction. I am currently working on designing and creating tools for older adults to improve their technology experience. I am also exploring designing to promote behavioral change. My work is under the supervision and direction of professor Debaleena Chattopadhyay at the Human-Computer Interaction Lab (HCI Lab) and the Electronic Visualization Lab (EVL).
Long-term exposure to air pollution can cause adverse health effects. Many efforts are underway to develop affordable, portable, and accurate technologies to help people monitor air pollution regularly. Although personal, wearable air pollution monitoring technologies are popular among some technology enthusiasts and citizen scientists, we know little about air pollution monitoring practices and references of lay individuals. We conducted a sequential explanatory mixed-methods study (n = 321) to understand people's current air pollution monitoring practices and their requirements for personal air pollution monitoring technologies. Although concerned about the adverse effects of air pollution (94%), less than 10% reported checking the levels of air pollution at least once a week. Respondents were more likely to carry a monitoring device as a bag accessory (74%) or wear it on their wrist (42%), than around their shoes, waist, or neck. If monitoring were available, however, it was unclear how much that would manifest behavior changes in individuals. We discuss how our findings can inform future technology design.
Recent epidemiological studies have shown that long-term exposure to air pollution is positively associated with mild cognitive impairment (MCI). Although interest in pollution monitoring is proliferating, self-tracking personal pollution exposure is little explored. In this thesis, I adopt a human-centered computing approach to explore the design space of personal pollution tracking wearables. This work makes three contributions to human-computer interaction: 1) design guidelines for rapid-prototyping low-cost, sub-optimal personal pollution tracking wearables and a physical prototype that measures PM2.5 and ambient noise which are the pollutants that epidemiological studies have demonstrated their association to MCI, 2) exploration of different calibration techniques to improve the accuracy of low-cost PM2.5 sensors, and 3) a characterization of how human interference, our day-to-day activities, significantly affect the operation of personal pollution tracking wearables. In sum, this thesis informs design guidelines about how to physically prototype personal pollution tracking wearables and where to wear them—beyond citizen-science efforts of data collection—rather toward monitoring personal long-term pollution exposures to mitigate the environmental risk factors for many illnesses such as early dementia.
Best Poster Honorable Mention
Recent epidemiological studies suggest that age-related cognitive decline---particularly, the stages between normal cognitive changes in aging and early dementia--- is adversely affected by environmental exposures, such as long-term air pollution and traffic noise. Although monitoring outdoor air pollution is now commonplace, and smart home solutions for monitoring indoor air quality is becoming prevalent, ways that the elderly can record long-term environmental exposures and adopt healthy lifestyle changes are little explored. We present myCityMeter, a pollution exposure management tool for older adults and their caregivers. myCityMeter measures the pollutants shown to be associated with cognitive impairment in older adults: PM2.5 and ambient noise. Using a set of neighborhood-level stationary and personal mobile sensors, myCityMeter helps users to monitor their environmental exposures, know potential exposures when planning activities, journal cognitive performances, and take day-to-day actions to avoid the environmental risk factors for early dementia.