Pilots

Restoring Functional Eating in Late Stage Dementia

Jon Dekar, DESIN LLC, Zackory Erickson, CMU Robotics Institute. DESĪN LLC will enhance its existing Obi assistive feeding robot with AI-driven attention monitoring and redirection capabilities to support self-feeding in individuals with AD/ADRD who struggle with inattention. The project aims to demonstrate technical feasibility and clinical utility in long-term care settings, ultimately reducing caregiver burden and improving quality of life for affected individuals.

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Measuring Heart Rate using Biomagnetism-based Wearable Devices

Longfei Shangguan, University of Pittsburgh This project will aim to develop an innovative wearable system that leverages biomagnetism to deliver more accurate heart rate and respiration monitoring across diverse skin tones.

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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.

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Leveraging AI for Just-in-Time Smartphone Solutions for Family Caregivers

Felipe A. Jain, Massachusetts General Hospital, Finale Doshi-Velez, Harvard University. Family caregivers of people living with dementia have high needs for skills training and methods to reduce stress. This project will study the feasibility of a just-in-time adaptive intervention delivered by smartphone to increase engagement and helpfulness of caregiver skills and relaxation content for caregivers.

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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.

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