Wearable & Implantable Technologies

Wearable & Implantable Technologies at Johns Hopkins

We build open, reproducible tools that turn data from wearable and implantable technologies into useful measures for health research.

Explore our work

A Johns Hopkins Biostatistics working group

WIT is one of the Department of Biostatistics working groups at the Johns Hopkins Bloomberg School of Public Health. The group adapts existing methods when possible and develops new methods when necessary to analyze data from wearable sensors used in health studies, including physical activity, blood glucose, and blood pressure.

Ciprian Crainiceanu

Ciprian Crainiceanu
Professor of Biostatistics

Vadim Zipunnikov

Vadim Zipunnikov
Professor of Biostatistics

John Muschelli

John Muschelli
Associate Research Professor of Biostatistics

Contact: Ciprian Crainiceanu (ccraini1@jhu.edu)

Meet the group Watch: WIT Working Group video

Research highlights

Walking, steps, and NHANES

Step counting, walking fingerprints, and open NHANES step data.

Large cohort studies

UK Biobank, NHANES, ARIC, BLSA, and STURDY: activity, mortality, dementia, and falls.

Functional data methods

Scalable methods for high-resolution sensor data.

Continuous glucose monitoring

Measures that capture the shape of glycemic variability.

Sleep and circadian rhythms

Sleep, chronotype, and wear time from wrist, hip, and chest devices.

Hemodynamics in cardiac surgery

Minute-level blood pressure and cardiac index linked to kidney injury and delirium.

All research directions

NoteFeatured: step counts for the nation

We benchmarked step counting algorithms on raw wrist accelerometry, applied them to nearly 15,000 NHANES 2011–2014 participants, and released minute-level step counts on PhysioNet. Read more about our NHANES work.

Latest news

All news

Learn with us

WearableR short course

Learn practical approaches for working with wearable-sensor data in R, from importing and organizing data to activity and sleep analyses.

Visit the WearableR short course

About

The WIT (Wearable and Implantable Technologies) group at Johns Hopkins develops methods and open-source software for measuring health in the real world. We work across sensor data processing, activity and sleep measurement, mobility, cardiovascular and physiologic signals, and transparent statistical workflows. Our goal is to help researchers move from raw device data to interpretable, reproducible evidence.

Our public code includes reusable R and Python tools, teaching materials, and research software. Visit the About & projects page to explore featured projects and their live documentation sites. All of our public repositories are in the jhuwit GitHub organization.