In Fall 2022, the Interactive Neurorehabilitation Lab moved from Virginia Tech to the University of Southern California. We are excited to develop our research with new colleagues in sunny Los Angeles and continue our meaningful collaborations with faculty and students at Virginia Tech, Arizona State University, Emory University, and the Shirley Ryan Ability Lab.
Author: akellihe
INR alum Dr. Setor Zilevu was named as one of the 35 under 35 Global Innovators by the MIT Technology Review Journal. Congratulations Setor on this prestigious recognition! Read more here!
Abstract in MIT TR:
“Setor Zilevu, 27, is working at the intersection of human-computer interaction and machine learning to create semi-automated, in-home therapy for stroke patients. After his father suffered a stroke, Zilevu wanted to understand how to integrate those two fields in a way that would enable patients at home to get the same type of therapy, including high-quality feedback, that they might get in a hospital. The semi-automated human-computer interaction, which Zilevu calls the “tacit computable empower” method, can be applied to other domains both within and outside health care,” he says.“
In March, our paper “Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications” was published in Sensors. Read the full text here.
Abstract:
For upper extremity rehabilitation, quantitative measurements of a person’s capabilities during activities of daily living could provide useful information for therapists, including in telemedicine scenarios. Specifically, measurements of a person’s upper body kinematics could give information about which arm motions or movement features are in need of additional therapy, and their location within the home could give context to these motions. To that end, we present a new algorithm for identifying a person’s location in a region of interest based on a Bluetooth received signal strength (RSS) and present an experimental evaluation of this and a different Bluetooth RSS-based localization algorithm via fingerprinting. We further present algorithms for and experimental results of inferring the complete upper body kinematics based on three standalone inertial measurement unit (IMU) sensors mounted on the wrists and pelvis. Our experimental results for localization find the target location with a mean square error of 1.78 m. Our kinematics reconstruction algorithms gave lower errors with the pelvis sensor mounted on the person’s back and with individual calibrations for each test. With three standalone IMUs, the mean angular error for all of the upper body segment orientations was close to 21 degrees, and the estimated elbow and shoulder angles had mean errors of less than 4 degrees.