The Johns Hopkins Artificial Intelligence and Technology Collaboratory for Aging Research, or JH AITC, has announced additional recipients of its fifth round of grant funding. Totaling just over $1 million, this incremental funding supports the development of artificial intelligence technologies to improve the health and quality of life of millions of older Americans and their caregivers.
Funded by the National Institute of Aging, the JH AITC has provided funding to more than 50 projects since its launch in 2021.
The new pilot projects include:
Identify Regulators, Druggable Targets and Indicators of Alzheimer's Disease (Martin Nwadiugwu, Tulane University): This project aims to identify differentially expressed transcriptional regulators involved in Alzheimer's disease that could serve as druggable targets, and to develop predictive models for cognitive impairment using multi-cohort single-nucleus RNA sequencing data.
AI-based Digital Cognitive Assessments for Early Detection of Dementia (Bin Huang, BrainCheck): BrainCheck Assess is a digital cognitive assessment tool for early detection of Alzheimer's disease.
Movement-Based Biomarkers of Aging Using Head-Worn Devices (Alexandra Hammerberg, Hominin Labs): The overall goal of this project is to translate ordinary human movement in the environment captured by commercially available head-worn devices into movement-based biomarkers of aging. In aging populations, these biomarkers can be used to identify early indicators of neurological disease and musculoskeletal decline, track fall risk, and predict exacerbations in health conditions allowing early intervention. This increased access to personalized, preventative health care will reduce clinical burden, thus reducing cost of care and helping aging Americans live longer, healthier lives.
Transforming Frailty Assessment: At Home-Based, AI-Driven Approach to Detect Pre-Frail Decline (Evan Haas, CurveAssure): This project will develop and validate a fully remote, video-based frailty monitoring system using CurveAssure, a HIPAA-compliant, AI-enabled platform that runs on consumer devices without specialized hardware or app downloads. CurveAssure integrates markerless motion capture, video-based strength estimation, 3D body-shape reconstruction, and patient-reported outcomes to quantify core frailty domains including mobility, strength, sarcopenia-related metrics, and fatigue. The overarching goal is to enable frequent, objective, at-home assessment of frailty and early detection of frailty progression in older adults.
AI-Enhanced Home Music Therapy with Wearables for Older Adults with Stroke (Preeti Raghavan, Johns Hopkins University): This project features a tablet-based app that utilizes music-supported therapy to enhance upper-limb recovery for stroke survivors.
Caregiver-Guided Assistive Robot for Aging at Home, or CARAH (Nilanjan Chakraborty, Stony Brook University): This project proposes a customizable assistive robotics framework that enables robots to acquire manipulation skills from small numbers of caregiver demonstrations by exploiting the mathematical structure of motion. CARAH emphasizes transparency and bi-directional feedback, allowing caregivers to incrementally train robots while receiving immediate feedback on the robot's ability to plan and generalize each task. This approach is designed to promote effective human–robot teamwork, with robots handling repetitive tasks and caregivers focusing on higher-quality interpersonal care.
Funds to support this AITC study were provided by the Johns Hopkins University AITC under award number P30AG073104. Learn more about the funded projects at this link.
Posted in Health, Science+Technology
Tagged robotics, alzheimer's disease, nih funding, dementia
