A multidisciplinary team of researchers is developing technology that can adapt therapy programs based on a patient’s needs and constraints.
The intelligent, closed-loop iRehab comprises modular, multisensor, multi-actuator robotic devices that analyze sensor data collected from an individual’s physiological performance, cognitive ability, and brain activity while he or she is doing rehabilitation.
“At its essence, the project is a learning rehabilitation system,” says project leader Fillia Makedon, Jenkins Garrett Distinguished Professor and director of the Heracleia Human-Centered Computing Lab. “It collects multisensory data and produces the best possible rehabilitation program guidelines for a patient.”
After assessing the data, a physical therapist or physician will be able to enter prescription drug history, diet record, and other information that may change over time to suggest a therapy program best suited to the patient.
“This makes rehabilitation treatment more effective and helps the clinician better understand a patient’s needs, thus personalizing medicine and health care, reducing costs, and putting the patient in control,” Dr. Makedon says. “It makes treatment decisions based on quantitative multisensing data while the person exercises and helps the medical experts gain a better understanding of the impact of rehabilitation on the person’s health. It can also provide remote monitoring, if needed.”
The project is part of a three-year, $1 million National Science Foundation grant. Researchers on the team include Heng Huang and Vassilis Athitsos from computer science and engineering; Robert Gatchel from psychology; and Mario Romero-Ortega from bioengineering.