The goal of Pilot Core for Aging (PCB) is to utilize a multidisciplinary collaborative approach to identify, develop, refine, and disseminate promising technologies that have high potential to improve the health and wellbeing of older Americans and/or their caregivers, with an emphasis on those that can mitigate current disparities in access and delivery of health care in rural and urban areas across the US.

Core Leadership:

Jeremy D. Walston, MD

Jeremy D. Walston, MD

Co-Principal Investigator
Administrative Core Pilot Core B Access to Underserved Populations of Older Adults JHU University-Wide Resources
Suchi Saria, PhD

Suchi Saria, PhD

Co-Director, Pilot B (Geriatrics)
Pilot Core B Core Leaders Engineering Resources

Core Activities:

  • To identify and fund innovative AI and machine learning-supported technologies that promise to improve the health and well-being of older adults through an annual pilot award process.
  • To ensure pilot projects are well-designed, timely and rigorous by providing intellectual leadership, oversight, and access to all AITC core resources.
  • To assist in the further development and translation of completed pilot projects into products that will benefit older adults and/or their caregivers.
  • To expand the expertise and network of funded investigators focused on aging relevant AI technologies.

Funded Projects

Virtual Apprentice (VA), a women-owned VR content development company, has developed a prototype platform, ReTreatVR™, designed to provide senior-friendly immersive experiences to support positive aging. Prototype experiences are designed to create happy memories, re-engage with the world, and enhance social interactions that can be shared and discussed with others. This will support positive aging and reduce social isolation.

A multidisciplinary team of ophthalmologists, engineers, and
entrepreneurs at Johns Hopkins University have developed a simple and inexpensive anterior segment imaging and telemedicine system to allow for remote eye screening facilitated by non-ophthalmologists. The system will be applied to address disparities in cataract screening, referral, and disease management for older adults in the United States. Partnerships will be formed with ophthalmology practices, senior living facilities, and long-term care facilities to further develop this platform and incorporate artificial intelligence in order to provide simple, accessible, and near real-time diagnosis and referral of vulnerable older adults with age-related cataracts.

PI: Kunal Parikh, Research Associate in the Department of Biomedical Engineering at the Whiting School of Engineering and the Johns Hopkins School of Medicine 

This project features a handlebar device that can be used to “train” seniors to improve their balance.The project will perform user testing of the Balance T device, a mechanical device designed to improve static and dynamic balance.  The Balance T device will be provided to volunteer participants age 60 and above interested in improving their balance, and will administer surveys to be completed post-use to assess usability of device, adherence to device use, and self-reported changes in balance.

Physical function, the ability to perform daily activities, is important to screen among geriatric patients since it can determine one’s independence and is linked to frailty, disability, and death. However, time and space constraints, lack of trained staff, and limited reimbursement mechanisms make it difficult for providers to screen for physical function. Additionally, patients are unable to accurately and consistently measure their own physical function; therefore, treatment plans will be affected. Wearable devices can collect mobility, balance, and gait data and can incorporate software to analyze physical function accurately without the need of supervision. We developed a Geriatric Function Assessment (GFA) system that effectively evaluates physical function among older adults. This GFA system will be incorporated into a wearable device that will perform motion and visual monitoring as patients navigate among common clinical areas during clinic visits. We aim to optimize the GFA’s system design and calibration, generate deep neural network models, and perform a feasibility design among 21 older adults with multiple chronic conditions in a clinic-based setting to maximize the user experience. We envision that the development of the GFA system provides a solution to both clinicians and geriatric patients by producing accurate data in an efficient manner.

Falls are associated with immobility, mortality, decreased independence; leading to social isolation and reduced quality of life. One of four individuals over 65 years old sustains a fall each year. While predictors of falls have been identified; including fall history, age, and cognition deficits, these data fail to provide insight in how to treat or track fall risk. As the aging population increases, the economic burden for hospitalization and supervision for those at risk for falls rises. A critical step toward improving our ability to treat fall risk is to identify technology to efficiently collect clinically-targetable and fall-relevant outcomes both in the clinic and the home. To achieve this goal, a novel, portable, and inexpensive shoe insert has been developed to measure standing and dynamic balance outcomes. The smart insole provides balance and gait parameters in real time, providing opportunity to collect highly quantitative fall predictors in the home or clinic and can facilitate the design of specific intervention to prevent future falls. The purpose of the project is to validate the insole technology and relate it to reported falls. Walking assessments will be conducted on 50 individuals with and without insoles and on a sub-sample of individuals wearing insoles in the community. Focus groups provide input from health professionals and insole users. Collectively, this project will allow us to 1) validate insole outcomes to in-lab gold-standards, 2) validate the clinical utility of outcomes by relating them to falls, and 3) validate the clinical usability of these outcomes by stakeholders.

 

This pilot will capture patient balance data using a force plate at 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. A large registry of balance data is key to using machine learning to aid in clinical assessment though a real time dashboard.

This will allow for identification of appropriate treatment options as well as implementing fall prevention strategies to create solutions and shift the focus from treatment to prevention.

This project will leverage our lightweight and affordable exosuits in concert with learning-based personalized control to promote physical activities in everyday settings and explore the potential to ultimately decrease the risk of cognitive decline which leads to dementia in older adults.