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.
Publication
International Congress of Parkinson’s Disease and Movement Disorders®, (MDS Congress), 2024
![Shovito Barua Soumma](/authors/shovito-barua-soumma/avatar_hube0f44c68b1adbeb577710805ef7c4fa_779815_270x270_fill_q75_box_center.jpg)
Graduate Research Associate
I am a Ph.D. student 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](/authors/hassan-ghasemzadeh/avatar_hu7b978ab76ad90ca51f18f381de7a3965_6873_270x270_fill_q75_box_center.jpg)
Director
Hassan Ghasemzadeh is an Associate Professor of Biomedical Informatics at Arizona State University (ASU) and a Computer Science Adjunct Faculty at Washington State University (WSU).