News & Events

Upcoming Webinar (May 27 at 4pm ET) – The Impact of Light Exposure on Sleep: A Pilot Study, Erik Page (Blue Iris Labs)

Registration Link: https://umass-amherst.zoom.us/meeting/register/VmPhXLckTb-PjSc1nnc6Mw About the Speaker:  Erik Page has been conducting research in lighting related fields for over 30 years including work involving light sources, light sensors, luminaires, lighting control systems and human factors. Lighting sensors, lighting control systems, and the impacts of light on human health have been a particular focus for the last decade where he became a recognized expert. His lighting controls and sensor work has included both the technology side (where Page has several patents) as well as the market and policy side (where he has several research publications). Over Page’s career, his focus has always been on “applied research” where emphasis has been placed on developing promising lighting technologies and transferring them from the laboratory to the marketplace, including during his 15 years at the University of California (10 years…

Continue ReadingUpcoming Webinar (May 27 at 4pm ET) – The Impact of Light Exposure on Sleep: A Pilot Study, Erik Page (Blue Iris Labs)

Past Webinar – Harnessing the Power of Technology to Change the Way We Age, Alex Glazebrook (OATS from AARP)

About the Speaker:  As VP of Programs, Alex Glazebrook is responsible for ensuring an outstanding experience for the older adults who participate in the many training and support programs that OATS offers. Alex earned a BA, MSW, and PhD from Stony Brook University, specializing in healthcare, with a focus on aging, investigating the many ways in which technology influences health outcomes for older adults. (https://oats.org/) Talk Abstract:  Our society is aging alongside a rapid increase in the evolution and advancement of technology that mediates daily life. The use and adoption of technology by older adults has increased in the recent past, but differences remain with their younger counterparts and the effects of digital disconnection in later life are more acute. This session will present recent technology adoption and use trends among older adults as well…

Continue ReadingPast Webinar – Harnessing the Power of Technology to Change the Way We Age, Alex Glazebrook (OATS from AARP)

AI-Driven Early Detection of AD Risk Using Speech Features

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. By validating these speech metrics and developing a predictive AI model, the study aims to enable earlier, more precise identification of individuals at risk for AD, facilitating timely intervention and personalized care.

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TRIALCHAT: Leveraging LLMs to enhance AD/ADRD clinical trial participation

Tim K. Mackey, S-3 Research LLC, Joshua Yang, California State University, Fullerton. This project will aim to develop TrialChat, an AI-powered chatbot and clinical trial navigator designed to increase participation in Alzheimer’s disease and related dementias (ADRD) clinical trials by providing tailored education, personalized trial matching, and recruitment support for older adults and caregivers.

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Leveraging Digital Cognitive Rhythms to Detect ADRD Risk in Family Caregivers

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. By integrating KeyWise AI's digital cognitive rhythm ("CogniRhythm") score and ki:elements' Speech Biomarker for Cognition (SB-C), this scalable, low-burden solution could identify and mitigate cognitive decline in this vulnerable population.

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Behavioral Analytics is the New Medical Device

Rhoda Au, Boston University, Laura McIntosh, EmPowerYu. This project will use in-home multimodal sensors to detect changes in daily life activity patterns that indicate fluctuations in cognitive status. This passive, low-burden method that captures behavioral symptoms is a significant opportunity for understanding how we can individualize the monitoring and treatment of chronic diseases like Alzheimer's Disease.

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