IEEE EMBS Distinguished Lecture: Paolo Bonato, Ph.D. on Wearables & Robotics in Rehabilitation
IEEE EMBS Distinguished Lecture: Paolo Bonato, Ph.D. on Wearables & Robotics in Rehabilitation
Are you curious about how cutting-edge robotics and machine learning are transforming rehabilitation medicine?
Join IEEE EMBS UIC, the student chapter of the Engineering in Medicine and Biology Society, for an exciting lecture by Paolo Bonato, Ph.D., a Harvard Medical School expert and leader in wearable tech, digital health, and precision rehabilitation. Dr. Bonato’s pioneering research explores how robotics and machine learning can retrain motor function for individuals with neurological conditions.
Date: Wednesday, February 26th
Time: Lecture 4:00 - 5:00 PM
Location: SEO Floor 10, Room 1000
Register Here: Interest Form
Why Attend?
-
Discover how predictive modeling can personalize rehabilitation interventions.
-
Learn about real-world applications of robotics for stroke and cerebral palsy patients.
-
Gain insights into the latest research on human-machine interaction and muscle synergy adaptation.
Don’t miss this opportunity to engage with a pioneer in rehabilitation technology and expand your understanding of cutting-edge biomedical innovations.
Reserve your spot and join us for an afternoon of inspiration and discovery. Let’s shape the future of healthcare together!
Open to all students, faculty, and staff. Bring your questions and curiosity!
We’ll see you there!
Date and Time
Location
Hosts
Registration
- Date: 26 Feb 2025
- Time: 04:00 PM to 06:00 PM
- All times are (UTC-06:00) Central Time (US & Canada)
-
Add Event to Calendar
- 851 South Morgan Street
- Chicago, Illinois
- United States 60607
- Building: Science and Engineering Offices
- Room Number: 1000
Speakers
Paolo Bonato, Ph.D. on Wearables & Robotics in Rehabilitation
In this talk, we will review the use of robotics in rehabilitation with a focus on retraining motor function in patients with neurological conditions ranging from stroke to cerebral palsy. We will argue that the prediction of patients’ response to robot-assisted motor training should account for the mechanisms underlying the short-term and long-term response of each individual patient to the forces generated by the robot. We will demonstrate how modeling the human-machine mechanical interaction and studying changes in muscle synergies in response to the forces generated by the robot could shed light on the ability of individual patients to display a positive response to the rehabilitation intervention.