Alzheimerâ€™s disease (AD) burdens the United States economy by at least $250 billion annually. Its prevalence is predicted to triple by 2050 unless further discoveries facilitate the prevention of the disease. Because of challenges in screening for AD, it often remains undiagnosed until cases advance and sufficient neuronal injury has occurred that reversal of the disease is unlikely. What is desperately needed is a cognitive screening tool that can objectively detect at-risk individuals and monitor the disease progression with no specialized staff or medical equipment. Driven by rapid advancements in wearable sensors and their growing social acceptability, this CAREER program will develop novel data analysis approaches required for the early detection of AD. Such a tool could initiate further biomarker testing and early interventions in order to alleviate neuronal damage before it becomes too late. In addition, an integrated educational and outreach program is designed to both foster interdisciplinary research training and to increase participation of underrepresented students in STEM disciplines. This project will create learning modules to target middle and high school students through school demonstrations, on-campus laboratory research, and summer experiences, with a focus on engaging female students at an early stage. It will integrate research outcomes in engineering curriculum with a focus on neurodegenerative diseases in order to ignite interest in engineering education among female and minority students. Knowledge gained from this project will be disseminated via research publications, tutorials, and workshops, as well as broader outreach activities to the area public library, science museum, and stakeholders.
This project is a major departure from current efforts to address current technical obstacles in individualized and longitudinal monitoring. Current data analysis approaches do not consider individuals' variability in their methods. They are trained using data from a well-characterized group of subjects and then applied to a new subject, regardless of differences. As a result, they do not consider the variation of patterns between subjects, which is critical for the detection of abnormal patterns due to the disease versus the differences within a subject. Additionally, none of the existing approaches consider intra-subject variations that may occur over time. This limitation is particularly critical for longitudinal monitoring of the cognitive impairment severity because these approaches are unable to distinguish longitudinal disease-related changes from changes due to aging. The planned approach to address these challenges in both data analytics and clinical practice is as follows. First, the project significantly improves the early detection of at-risk individuals during initial assessments of cognitive decline by developing innovative individualized deep learning approaches. These novel approaches will be based on an encode-decoder architecture to explore raw gait data for features related to cognitive decline, while a novel domain adaptation technique will be developed to customize the architecture according to an individual's variability. Second, it significantly improves the detection of the disease progression rate by developing novel adaptive deep learning approaches for longitudinal monitoring of cognitive decline. The approach will be based on deep learning methods with heterogeneous data fusion ability to enable the analysis of free-walking gait, along with speech data. A novel unsupervised domain adaptation based on reinforcement learning techniques will be developed to customize the model according to the intra-subject variations over time. The key transformative aspect of the proposed research is the development of data analytics that are based on available sensor data, but which provide individualized models that show change over time.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.