Ziwei Ma
University of Tennessee-Chattanooga, TN·Biomedical Engineering
Traumatic brain injury, autism spectrum disorder and other neurologic conditions can disrupt movement, balance and coordination, making daily life harder. Rehabilitation can last months or years.
- Fields
Biomedical Engineering
Engineering
Computer Science
- Topics
wearable sensors
rehabilitation monitoring
remote rehabilitation
- Grants
- 1 known grant, through Sep 2030
What they work on
- Develop energy-efficient sensing approaches and feature-extraction methods for multimodal wearable sensors, to capture informative movement signals during rehabilitation outside the clinic.
- Build patient-specific machine learning methods for high-dimensional time-series sensor data, to identify movement patterns linked to neurologic motor disorders and create quantitative biomarkers of recovery.
- Compare wearable sensor measurements with established clinical evaluations in people with traumatic brain injury and autism spectrum disorder, and assess the feasibility of the remote rehabilitation monitoring framework.
More about the research
However, progress is usually checked only during clinic visits, so clinicians know little about how patients move and function at home. This collaborative project explores whether wearable sensors combined with artificial intelligence (AI) tailored to each patient can track recovery objectively outside the clinic.
Why it matters
Objective, at-home measures of movement recovery could support clinicians treating people with traumatic brain injury, autism spectrum disorder and other neurologic motor disorders. These measures could add to clinic-based evaluations and guide individualized rehabilitation. They could also widen access to remote rehabilitation and help flag delayed recovery earlier.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Najim Dehak
Johns Hopkins University, MD·Engineering
Provide engineering and technology expertise, including mobile digital health tools, robotics and AI, to support translational research on frailty in older adults.
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.
Erik Henricson
University of California-Davis, CA·Biomedical Engineering
Test whether movement performance, community mobility and patient-reported independence and well-being reflect one shared mobility process, using integrated computational models.
The school
University of Tennessee-Chattanooga: 2 labs on PI Pages, $12M a year on research, 14 research doctorates a year.
Reach out
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