Marziye Eshghi, MGH Institute of Health Professions This project will leverage AI-driven analysis of remotely collected speech data to detect early signs of Alzheimer’s disease (AD) by linking speech acoustic and kinematic features to AD molecular pathologies.
Raeanne C Moore, UCSD, Yeonsu Song, UCLA. This project will develop and pilot machine learning algorithms to passively monitor cognitive fluctuations among family caregivers of persons living with dementia by analyzing their smartphone typing patterns and speech.
Chaitanya Gupta, ProbiusDX Inc., Steven Arnold, Massachusetts General Hospital. This project will use Probius QES, a novel technology that measure molecular vibrations in blood plasma samples, to identify and differentiate healthy aging vs Alzheimer’s Disease progression in a well characterized cohort of 1000+ individuals from MassGen.
Joyita Dutta, UMass Amherst. This project will develop AI techniques for at-home sleep staging in seniors using multimodal data from EEG headband and smartwatch devices.
Honghuang Lin, UMass Chan Medical School. This pilot project explored the use of wearable accelerometers to passively monitor walking cadence as a potential early indicator of cognitive decline in older adults.
Quan Zhang, Massachusetts General Hospital. This project will create an adapted version of NINscan, a Near-Infrared Neuromonitoring Device, with the goal of enabling older adults and AD patients to collect high quality brain and physiological data at home.
Jennifer Stamps and Kyle Rand, Rendever, Inc. This pilot study will evaluate the utility of RendeverFit™, a VR fitness platform, in terms of its acceptance by and relevance to older adults, as well as collect data to support the construction of machine learning models.