Erik Larsen, Sonde Health. Brad Dickerson, Massachusetts General Hospital.
Bonnie Wong, Massachusetts General Hospital. The pilot project, conducted by researchers from Massachusetts General Hospital and Sonde Health, explored the use of a vocal biomarker platform to detect and monitor cognitive impairment in older adults.
- Year 1
Jen Blankenship, VivoSense Inc. Michael Busa, UMass Amherst. This pilot study aimed to develop and validate new algorithms for detecting walking behavior using wearable sensors in older adults, including those with Alzheimer’s disease or mild cognitive impairment.
- Year 1
Ipsit Vahia, McLean Hospital.
Rachel Sava, McLean Hospital. The ADAPT pilot study explored the use of wearable sensor technology to support psychopharmacological care in individuals with Alzheimer’s disease and related dementias.
- Year 1
Amanda Paluch, UMass Amherst. Dae Hyun Kim, Hebrew SeniorLife. Rags Gupta, Butlr Technologies Inc. This AITC pilot project explored the use of non-invasive, ceiling-mounted heat sensors to detect frailty in older adults living in senior communities.
- Year 1
Edward Jay Wang, UCSD. The pilot project focused on developing and validating BPClip, a low-cost, smartphone-based blood pressure monitoring device.
- Year 1
Anna Barnacka, MindMics Inc. The MindMics pilot project explored the use of infrasonic hemodynography (IH) technology embedded in everyday wireless earbuds to monitor cardiovascular health and assess vascular aging.
- Year 1
Jane Saczynski, Northeastern University. Edward Marcantonio, Beth Israel Deaconess Medical Center. Acute illness presents in the most vulnerable organ in the body, among patients with dementia that organ is the brain and acute illness often presents first as delirium, an acute confusional state. This project will evaluate home monitoring devices as early indicators of acute illness in persons with dementia.
- Year 1
Joseph Chung, Kinto. The Kinto pilot project explored how AI technologies—specifically sentiment analysis and generative language models—could enhance support for family caregivers of individuals living with Alzheimer’s and related dementias (ADRD).
- Year 2
James Mastrianni, The University of Chicago. Josh Kim, Adiona Health. This project seeks to validate a novel machine learning technique that analyzes motion data from an accelerometer in an Apple Watch to identify the onset of an agitation episode in a person with ADRD.
- Year 2
Robert Hager, Preveal Technologies, Inc. Hassan Ghasemzadeh, ASU. The Preveal pilot project focused on developing an in-home cognitive improvement system using EEG-based neurofeedback (EEG-NFB) to monitor and enhance working memory in older adults.
- Year 2
Ryan Gooch, TellUs You Care. Rebecca Spencer, UMass Amherst. This study will test Tellus's machine learning algorithms for tracking physiological and behavioral status using data from Tellus' radar device.
- Year 2
Erik Page, Blue Iris. The Blue Iris Labs pilot study investigated how personal light exposure affects sleep quality using a new wearable light sensor called the Speck.
- Year 2
Dave Keeley, Electronic Caregiver, Inc. Michael Busa, UMass Amherst. This research project will enhance Electronic Caregiver's Addison Care system with computer vision methods for evaluating functional strength, stability, and falls risk in older adults.
- Year 2
Micheal Milburn and William DeJong, Impairment Science, Inc. Anuj Pradhan and Shannon Roberts, UMass Amherst. The Druid app uses multiple divided attention tasks to assess cognitive-motor behaviors related to driving. This project aims to validate the Druid app for use by older adults aged 64-85 years.
- Year 2
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.
- Year 2
Archna Bhatia, Institute for Human & Machine Cognition, George Sperling, UCI. This project aims to develop an Intelligent Cognitive Assistant that provides real-time word retrieval support as well as personalized training to enhance word retrieval for individuals with mild cognitive impairment and Alzheimer’s Disease and Related Dementias.
- Year 2
Jennifer Williams, Stanford University. This project will develop and test an approach to monitoring older adults with chronic obstructive pulmonary disease in the home using an unobtrusive metric derived from acoustic features collected during normal cell phone use.
- Year 2
Amar Basu, Wayne State University, Michael Busa, UMass Amherst. This project will evaluate TRACE, a novel wearable sensor for monitoring orthostatic vital signs continuously at home, whenever an individual stands up.
- Year 2
Richard Watkins, Livindi. The Livindi pilot project aimed to develop and test a technology platform that detects distress-related events—especially falls—using audio recognition, motion sensors, and machine learning. Participants received pre-configured kits with tablets and sensors, and the system was designed to operate entirely on-device to ensure privacy.
- Year 2
Rebecca Spencer, UMass Amherst. The pilot project aimed to validate the accuracy and usability of commercial sleep tracking devices in older adults, including those with Alzheimer’s disease (AD), related dementias (ADRD), or mild cognitive impairment (MCI).
- Year 2
Mark Eldaief, Massachusetts General Hospital. This project will establish a “technology-ready cohort” of individuals with Alzheimer’s Disease and related dementias to support the evaluation of digital assessments relevant to ADRD patients and their caregivers.
- Year 2
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.
- Year 2
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.
- Year 2
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.
- Year 2
Ipsit Vahia, Rachel Sava, McLean Hospital. Joseph Chung, Rippl. The CAREALL pilot project explored the use of large language models (LLMs) to support caregivers of individuals with dementia.
- Year 3
Xian Du, Joohyun Chung, UMass Amherst. Shishir Prasad, BD.
In this project, we will develop the approach for the continuous monitoring of long-term care (LTC) resident’s behavioral and physiological signals over extended durations using cameras and wearable sensors.
- Year 3
Jennifer Flexman, Moneta Health. Michael Busa, UMass Amherst. This project will develop AI algorithms used by Moneta™ digital therapy assistant to monitor the speech of individuals with mild cognitive impairment and early dementia during cognitive rehabilitation therapy.
- Year 3
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.
- Year 3
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.
- Year 3
Jennifer Rae Myers, Chelsea S. Brown, Musical Health Technologies. This pilot project focused on developing an equitable machine learning (ML)-based music intervention for older adults at risk for Alzheimer’s disease. The study progressed through two phases, beginning with IRB approvals and participant recruitment in mid-2024.
- Year 3
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 (AD/ADRD) to engage in oral self-care in at-home settings.
- Year 3
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.
- Year 3
Gang Wang, University of Illinois at Urbana-Champaign. Roopa Foulger, OSF. This project will design, prototype, evaluate, and potentially deploy an AI-enabled voice agent to assist patients (especially older adults) to better recognize phishing messages and reduce cybersecurity risks during patient outreach and communications.
- Year 3
Hamed Tabkhi, Mona Azarbayjani, ForesightCares Inc. Sanjay Iyer, Memory & Movement Charlotte. The pilot project focused on developing and validating an AI-based visual assessment (AVA) app for at-home monitoring of motor function in older adults with Parkinson’s Disease (PD).
- Year 3
John Ralston, Neursantys Inc. VP Nguyen, UMass Amherst. This project will develop ML-driven methods to adapt EVS stimulus parameters to each patient’s unique sensory and motor impairment profile to increase the effectiveness of NEURVESTA’s current treatment protocol.
- Year 3
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.
- Year 4
Jeremy Linsley, Operant BioPharma. This project will use a proprietary robotics and artificial intelligence to identify targets and clinically-tested drugs that could be repurposed for Alzheimer's disease by blocking the harmful interaction between Tau and Amyloid Precursor proteins.
- Year 4
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.
- Year 4
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.
- Year 4
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.
- Year 4
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.
- Year 4
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.
- Year 4
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.
- Year 4
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.
- Year 4
Mariano I. Gabitto, Allen Institute. This project seeks to develop and train a novel machine learning foundational model that unifies brain and peripheral immune system omics data to identify blood biomarkers and map cellular changes in AD/ADRD.
- Year 5
Selina Zhu, Lumia Health. Paolo Bonato, Spaulding Rehabilitation Hospital. This pilot will test the Lumia ear wearable in people with Alzheimer’s disease and related dementias (AD/ADRD), allowing users to track blood flow to the head and report symptoms through a voice-controlled AI assistant. The study will focus on usability and adapting Lumia’s technology for older adults with cognitive impairments.
- Year 5
Justin Chan, Carnegie Mellon University. Swarun Kumar, Carnegie Mellon University. Neelesh Nadkarni, University of Pittsburgh. The proposed work uniquely aims to measure pulse transit time and blood pressure across different arterial points across the body using the reflections of wireless signals from a single AI-enabled mmWave radar device, which is a key enabler towards whole-body blood flow monitoring both in home and clinical environments.
- Year 5
Gregory Stock, Socratic Sciences Inc. Hamed Zamani, University of Massachusetts. Mary Mittleman, New York University. Socratic Sciences, in collaboration with UMass and NYU, is developing an AI-facilitated, question-driven app to help family caregivers of those with AD/ADRD connect and support one another in small, trusted groups around open-ended questions. The project will test and refine a group-facilitation AI bot to make meaningful peer support scalable, accessible, and affordable for millions of overwhelmed caregivers.
- Year 5
Wenyao Xu, Auspex Medix. Wei Sun, University at Buffalo. This project is to investigate an AI-powered, smartphone-based hearing screening tool that uses non-volitional pupillary responses to objectively assess hearing functions and loss.
- Year 5
Eun Kyoung Choe, University of Maryland-College Park. This pilot develops Aidara, an AI-powered digital health system that helps caregivers of individuals with cognitive impairments make safer over-the-counter medication decisions. Through multimodal interaction and personalized guidance, Aidara aims to support informed decision-making and reduce medication-related risks.
- Year 5
Jesse Poganik, Brigham and Women's Hospital. This pilot project leverages artificial intelligence and advanced aging biomarkers to identify existing FDA-approved medications that may slow biological aging, using clinical data and biospecimens from over 155,000 participants in the Mass General Brigham Biobank. By focusing on approved drugs with established safety profiles, this approach enables rapid translation of findings into clinical practice for promoting healthy aging and preventing age-related diseases.
- Year 5
Katie Seaver, The Babel Group. Rachel Khasky-Levy, The Babel Group. This project will evaluate a personalized speech recognition app built by Voiceitt for adults aged 55+ with severe dysarthria, focusing on accuracy, usability, and user satisfaction. Participants will train the app on their own speech and use it in daily life, with support from The Babel Group.
- Year 5
Pamela Z. Cacchione, University of Pennsylvania. Li Shen, University of Pennsylvania. Heart Failure Monitoring Socks will be used in hospitalized persons with heart failure to gather data on heart failure exacerbations and responses to treatment. We will use this data to develop predictive models for edema and fatigue due to heart failure.
- Year 5