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Uses Channel State Information (CSI) from existing WiFi devices and new high-rate sampling to distinguish stands, sits, laying, and falls; aims to reduce costs and improve equity vs. cameras, radar, or pendants.
EmailFalls are the leading cause of injury for adults 65+. This project advances WiFi motion sensing beyond presence/activity to classify types of stopped movement—specifically detecting falls—by increasing sampling rates and applying novel AI for CSI pattern recognition. The approach avoids privacy issues, professional installs, and compliance burdens, with potential to upend the PERS market and broaden access in underserved communities.
Founder of Care Daily developing AI-powered caregiving and home monitoring tools.
Harvard psychiatrist researching digital markers of mood and cognitive disorders.
Neuropsychologist at Johns Hopkins studying cognitive and neuropsychiatric measurement in aging.
CEO of CareYaya using AI to scale caregiver and elder support technologies.
Johns Hopkins expert in vestibular and balance rehabilitation for older adults.
McLean Hospital researcher studying digital tools for cognitive and emotional health in aging.
