AI-Powered Detection of Freezing of Gait Using Wearable Sensor Data in Patients with Parkinson’s Disease [Abstract]

Abstract

This study developed a novel patient-independent, cost-effective AI model for detecting Freezing of Gait (FoG), using a single wearable sensor and without the need for model retraining in new patients. This approach is expected to reduce patient burden and enhance clinical adoption of the technology. Using a single accelerometer and a rigorous validation methodology, we address individual variability in gait and demonstrate model’s generalizability through cross-validation methods.

Shovito Barua Soumma

Graduate Research Associate

I am a Ph.D. candidate at Arizona State University. I work as a Graduate Research Associate at Embedded Machine Intelligence Lab (EMIL) under the supervision of Dr. Hassan Ghasemzadeh.

Hassan Ghasemzadeh

Director

Hassan Ghasemzadeh is a Professor of Biomedical Informatics at Arizona State University (ASU).