News & Events

Poster Presentation: 2026 Alzheimer’s Association International Conference

Title: Digital signatures of Alzheimer’s disease risk among Hispanic/Latino adults in the United States, showing how passive smartphone and wearable data can capture real-world behavioral markers of cognitive and cardiovascular risk. Authors: Raeanne Moore, Andrea Mendez Colmenares, Emma Churchill, Tess Filip, Linda C. Gallo, Alexander P. Demos, Erin E. Sundermann, Douglas R. Galasko & Maria Marquine Title: Daily Cognitive Function in Dementia Assessed Using Smartphone-Based EMA: Links with Dyadic Sleep Authors: Yeonsu Song, Laura Campbell, Brent Mausbach, Jennifer L. Martin, Mary-Lynn Brecht, Raeanne Moore Title: Day-to-day Memory Fluctuations in MCI: Linking Subjective Concerns to Real-World Performance Using Mobile Cognitive Assessment Authors: Laura M. Campbell, Emma Parrish, Colin A. Depp, Robert Ackerman, Philip D. Harvey, Amy E. Pinkham, Raeanne C. Moore

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Grant Funding: Making Obstetric Care Smart

Justin Chan's lab received ARPA-H funding to design the AI/ML algorithms for a wearable monitoring system to better identify fetal distress and its cause, enabling a safer labor and delivery experience for mothers and babies. The system, called OMEGA, or Optical, Mechanical, and Electrical Global Assessment of fetal hypoxia, aims to replace 50-year-old, indirect, unreliable fetal heart rate monitoring technology with a unified, real-time assessment of fetal oxygen delivery and adaptive capacity. Source: https://engineering.cmu.edu/news-events/news/2026/06/23-transform-childbirth-care.html

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Oral Presentation: Best Practices for Digital Phenotyping Research in Aging Populations

This is part of the monthly MassAITC webinar series. Abstract: Digital phenotyping is transforming aging research by enabling high-frequency, real-world measurement of cognition, behavior, symptoms, and context through smartphones, wearables, and passive sensing technologies. This talk will review how digital health tools can complement traditional clinic-based assessments by capturing intraindividual variability, diurnal patterns, environmental influences, and subtle changes in cognitive and functional performance that may signal risk for neurodegenerative disease. Using examples from studies of healthy aging, MCI, Alzheimer’s disease risk, dementia caregiving, and related clinical populations, the talk will highlight best practices for designing digital phenotyping protocols, balancing participant burden with data richness, maximizing adherence, integrating active cognitive assessments with passive data streams and biomarkers, and applying analytic approaches that distinguish within-person change from between-person differences. The session will emphasize opportunities for digital phenotyping…

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Oral Presentation: Geroprotectors Hiding in Plain Sight: Systematic Identification of Approved Drugs that Reduce Organ-Specific Biological Age

Invited Speaker at the 2026 Systems Aging Gordon Research Conference titled: Complexities of Aging Across Species, Evolution, Reproduction, Human Longevity and Frailty Source: https://www.grc.org/systems-aging-conference/2026/

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Publication: Measuring multi-site pulse transit time with an AI-enabled mmWave radar

Authors: Jiangyifei Zhu, Kuang Yuan, Akrash Prabhakara, Yunzhi Li, Gongwei Wang, Kelly Michaelsen, Justin Chan & Swarun Kumar Abstract Pulse Transit Time (PTT) is a measure of arterial stiffness and a physiological marker associated with cardiovascular function, with an inverse relationship to diastolic blood pressure (DBP). We present an AI-enabled mmWave system for contactless multi-site PTT measurement using a single radar. By leveraging radar beamforming and deep learning algorithms our system simultaneously measures PTT and estimates diastolic blood pressure at multiple sites. The system was evaluated across three physiological pathways – heart-to-radial artery, heart-to-carotid artery, and mastoid area-to-radial artery – achieving correlation coefficients of 0.75–0.86 compared to contact-based reference sensors for measuring PTT. Furthermore, the system demonstrated correlation coefficients of 0.90–0.91 for estimating DBP, and achieved a mean error of -0.62–0.06 mmHg and standard deviation…

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