Najim Dehak
Johns Hopkins University, MD·Engineering
Frailty, the loss of strength and resilience that makes older adults vulnerable to falls, illness and disability, is hard to measure and hard to prevent. Wearable sensors, mobile health tools, robotics and artificial intelligence (AI) could help, but engineers and geriatric clinicians rarely work side by side.
- Fields
Engineering
Computer Science
Medicine & Clinical Research
- Topics
frailty
older adults
aging
- Grants
- 1 known grant, through Jun 2028
What they work on
- Provide engineering and technology expertise, including mobile digital health tools, robotics and AI, to support translational research on frailty in older adults.
- Support development of technology-assisted measures of gait, activity and other functional signals, and AI methods that turn these signals into frailty assessments.
- Move frailty-focused technologies, such as fall-prevention and medication-assistance tools, into intervention and prevention clinical studies, including two pilot studies.
- Mentor junior investigators in frailty research and ensure that technologies developed in the core reach underserved older adults.
More about the research
This core aims to connect them so that new technologies for frailty can be built, tested and put into use.
Why it matters
Frail older adults face high risks of falls, poor treatment adherence and loss of independence. Technologies that measure gait and activity, apply AI to frailty assessment, assist with medication or prevent falls could improve diagnosis and care. Remote tools could extend that care to underserved older adults who have trouble reaching clinics.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Zhaoshuo Jiang
San Francisco State University, CA·Engineering
Develop a robust data-driven classification model that detects walking events from floor vibration signals recorded by sensors in a home.
Alexis Battle
Johns Hopkins University, MD·Computer Science
Mentor early-stage investigators in study design, data pipelines and quantitative methods for multimodal, longitudinal data in aging and Alzheimer's disease research.
Ziwei Ma
University of Tennessee-Chattanooga, TN·Biomedical Engineering
Develop energy-efficient sensing approaches and feature-extraction methods for multimodal wearable sensors, to capture informative movement signals during rehabilitation outside the clinic.
The school
Johns Hopkins University: 100 labs on PI Pages, $4.1B a year on research, 622 research doctorates a year.
Reach out
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