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

International Congress of Parkinson’s Disease and Movement Disorders®, (MDS Congress), 2024
Shovito Barua Soumma
Shovito Barua Soumma
Graduate Research Associate
PhD Student

Currently I am working on building and optimizing deep learning models for wearable sensors data.