Erik Henricson
University of California-Davis, CA·Biomedical Engineering
Being able to get around lets people work, learn, see friends and live on their own. Neuromuscular disorders, neurological diseases and aging can slowly take that ability away.
- Fields
Biomedical Engineering
Medicine & Clinical Research
Computer Science
- Topics
human mobility
ambulation
gait
- Grants
- 1 known grant, through Sep 2030
What they work on
- Test whether movement performance, community mobility and patient-reported independence and well-being reflect one shared mobility process, using integrated computational models.
- Combine wearable inertial sensors, home-based markerless motion capture, GPS-based community travel measures and patient-reported outcomes to describe mobility in everyday settings.
- Identify hidden factors underlying mobility, physical activity, community participation, independence and well-being, using explainable supervised and unsupervised machine learning.
- Evaluate whether these mobility factors hold across children, adults, older adults and people with neuromuscular or other mobility-limiting conditions.
More about the research
Researchers can measure walking speed, daily activity, how far people travel and how they feel about their lives. But these are usually measured separately, and it is still unclear how they combine into a person's real-world mobility. This project asks how movement, the places people go and their participation in community life fit together as a single mobility process across the lifespan.
Why it matters
People with neuromuscular disease, neurological conditions or age-related decline lose mobility in ways that standard clinic measures only partly capture. A combined, scalable way to measure real-world mobility could improve how rehabilitation and treatments are evaluated. It could also help clinicians and researchers track independence and community participation across the lifespan.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Zhaozheng Yin
Stony Brook University, NY·Computer Science
Develop motion capture from wearable inertial measurement unit (IMU) sensors alone to reconstruct and predict daily-life gait, using knowledge distillation from a multi-sensor teacher network to a simpler student network.
Zhishan Guo
North Carolina State University, NC·Engineering
Develop a gait capture method using only wearable inertial motion unit (IMU) sensors, using knowledge distillation so a multi-sensor teacher network trains a simpler IMU-only student network to predict walking in daily life.
Sunghoon Lee
University of Massachusetts, MA·Biomedical Engineering
Develop a Body Channel Identification (BCID) sensing platform, with batteryless object tags read by a wrist-worn device through the body during touch, that records object identity, contact timing, grip pressure and wrist motion.
The school
University of California-Davis: 49 labs on PI Pages, $1.0B a year on research, 647 research doctorates a year.
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