The Johns Hopkins Artificial Intelligence and Technology Collaboratory for Aging Research, or JH AITC, has announced the last cohort of awardees that will receive support from its second round of grant funding. Totaling just over $1 million, this round supports the collaboratory's mission by funding the development and implementation of artificial intelligence technologies to improve the health and well-being of older adults.
Four additional applicants from academia, industry, and clinical practice were selected for funding late last year. The awardees will each receive up to $200,000 in direct costs over a one-year period, as well as access to resources and mentorship from university experts.
Launched in 2021 with a $20 million grant from the National Institute on Aging, the JH AITC is a national hub for innovations in healthy aging and cross-disciplinary collaboration within the Johns Hopkins community and beyond.
"The JH AITC continues to push the boundaries on new and repurposed smart technologies to improve the lives of older Americans in the current round of pilot grants," says Phillip Phan, a professor of strategy and entrepreneurship at the Carey Business School and the director of the JH AITC's Networking and Engagement Core.
The four additional pilots funded by this round focus on promoting older adults' independence through ambulatory assistance and mental health maintenance. Chien-Ming Huang, a professor of computer science at the Whiting School of Engineering, and Junxin Li, an associate professor at the Johns Hopkins School of Nursing, propose an innovative approach to enhance older adults' mobility and quality of life: They want to develop AI-powered, lightweight, and affordable exosuits to assist users in walking.
This interdisciplinary endeavor bridges the gap between advanced robotics, AI, and geriatric care. Joined by Hao Su, an industry collaborator from Picasso Intelligence, the team plans to develop a learning-based personal control system for exosuit-enabled walking assistance, evaluate the intervention's effectiveness in a community study, and translate the product into a viable product for home use.
"By focusing on personalized mobility aids, we expect to demonstrate significant improvements in mobility, independence, and overall quality of life for older adults—not only benefitting individual users but also resulting in broader implications for health care practices and policies related to aging populations," says Huang.
In another pilot led by Johns Hopkins researchers, a team of clinical psychiatrists at the Johns Hopkins School of Medicine—Muhammad Haroon Burhanullah, an assistant professor of psychiatry and behavioral sciences, and Paul Barton Rosenberg, a professor of psychiatry and behavioral sciences and the co-director of the Memory and Alzheimer's Treatment Center in the Division of Geriatric Psychiatry and Neuropsychiatry—are collaborating with medical technology company EyeControl to combat the negative health effects of delirium in ICU patients.
Delirium is an altered state of consciousness, characterized by episodes of confusion, that can develop over hours or days. And for older adults, it's a major problem that results in poor outcomes like increased mortality, longer hospital stays, higher rates of cognitive decline, and increased health care costs.
However, effective communication and reorientation interventions have been shown to help reduce delirium, according to the pilot researchers. But for nonverbal patients, communication like this becomes a bit trickier. To solve this problem, the team plans to use EyeControl's eye-tracking wearable communication device and associated AI platform to detect and manage delirium in an ICU study.
"By enabling communication throughout the patient recovery spectrum, this effort could enhance adherence to current delirium treatment guidelines, potentially improving patient outcomes such as mortality, mechanical ventilation use, coma, delirium, dementia/cognitive impairment, restraint-free care, ICU readmissions, and post-ICU discharge disposition," the researchers say.
Other pilots funded this round include:
- Automatic Assessment of Neuropsychiatric Symptoms Using Non-Intrusive Contactless Ambient Intelligence Technologies (Ehsan Adeli, Stanford University): This pilot plans to develop automated artificial intelligence methods to detect and track an individual's neuropsychiatric symptoms (NPS) using non-intrusive, privacy-preserving ambient sensors at home. The software will document the sequence of occurrences of NPS, predict adverse events, and provide interpretable data for accurate diagnosis during a clinical visit.
- Predicting Fall Risk in Older Adults Using Machine Learning (Rita Patterson, University of Texas Health Science Center): This pilot plans to collect patient balance data via force plate at the point of care in a geriatric outpatient clinic to provide insight into what medical conditions and social determinants of health influence balance and underlying risk of injuries for older adults. This algorithm will be deployed at the point of care to inform clinical providers of at-risk individuals so that intervention strategies can be discussed with their patients.
"These technologies from around the U.S. will help clinicians and families detect neurological decline before symptomatic presentation," says Phan. "The goal is to support early interventions that increase independence."