Ph.D. to be and HCI Researcher
Hello! I am Nina Sakhnini. I am a Computer Engineer, a researcher and a Computer Science graduate student at the University of Illinois at Chicago. My research interest is in Human-Computer Interaction. I am currently working on my project titled “myCityMeter: Helping Older Adults Manage the Environmental Risk Factors for Cognitive Impairment” this project explores designing and creating a wearable for older adults that works side by side with a mobile application for Android platform to guide the older adults throughout their daily activities to avoid areas with high noise and PM 2.5 pollution. The project’s motivation is that Epidemiological studies have shown that prolonged exposure of older adult to noise and PM 2.5 pollution is is adversely affecting their Cognitive state. This project is under the supervision and direction of professor Debaleena Chattopadhyay at the Human-Computer Interaction Lab (HCI Lab) and the Electronic Visualization Lab (EVL). I just finished my MS degree and I am planning to go for a Ph.D..
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.
People with mild to moderate Dementia have several forms of cognitive impairments, one of which is memory impairment. Dementia patient with memory impairments have difficulties finding the right words in a sentence when engaged in a conversation. To help people with Dementia recall words and concepts in everyday conversations, our research is exploring how to design a proactive digital aid. We are designing an ambient speech-recognition system that will be listening to the user’s conversations (when actively switched on). By listening to user’s conversation, the system will intelligently detect the need of a memory trigger. Such as if the user forgets a name, a place, or an event. Also, the system will give the user context-based memory-refreshing trigger. For example, if the conversation was regarding a certain topic, such as polar bears, the system will display content to help the user remember what a polar bear is, and some related information about polar bears. We are exploring different modalities to provide memory trigger such as visual, vocal, and video-based content. Sensing (of ongoing talk) can be turned on or off at demand. Our system will help in triggering memory cues for people with Dementia and thereby may improve the quality of the everyday life. We are requesting Azure App Service for our research to process speech in the cloud and send triggers to users’ personal devices.