News & Events

Hamed Tabkhi, Mona Azarbayjani, ForesightCares Inc. Sanjay Iyer, Memory & Movement Charlotte. The project aims to conduct a feasibility study on utilizing an AI-based visual assessment application to assess the balance, gait, and hand movements of older adults with Parkinson's disease (PD) within their home environments, focusing on underserved communities in Charlotte. The ultimate objective is to eliminate barriers to digital health equity and enhance access to marginalized older adults with PD.

Edward Jay Wang, Billion Labs Inc. This project aims to establish accessible early screening of hypertension by democratizing BP monitoring. We aim to achieve this by converting the billions of smartphones into oscillometric BP monitors without hardware add-ons.

Inbal Billie Nahum-Shani, University of Michigan. Vivek Shetty, UCLA. Guy Shani, Michigan State University. Susan A. Murphy, Harvard University. This project seeks to develop a novel AI-powered digital tool to empower older adults with Alzheimer's disease and related dementias (ADRD) to engage in oral self-care in at-home settings. This Dyadic Digital Coach (DDC) will leverage the untapped potential of dyadic relationships between older adults and their primary caregivers.

Jennifer Rae Myers, Chelsea S. Brown, Musical Health Technologies. This project will use machine learning to enhance cultural relevance and usability of a therapeutic mHealth app (SingFit) for the purpose of improving cognitive and emotional health in at-risk older adults.

Ehsan Adeli, Victor W. Henderson, Stanford University. The proposed project aims to design a mobile app that not only instructs and records individuals performing Short Physical Performance Battery (SPPB) tests but also uses these data for predictive analysis to monitor and quantify the risk of cognitive impairment over time, utilizing video data analyzed for motor-cognitive relationships.

G. Antonio Sosa-Pascual, REOFTech. Michael Busa, UMass Amherst. This project aims to collect data from wearables and smart home sensors to determine the state, rate, and direction of change in dementia patient agitation and caregiver well-being. Analyzing the strength, directionality, and temporal relationship between the classifiers from each specific dyad member will allow our platform to provide novel insights and actionable intelligence for both the dyad and their clinical care providers.