Wearable Online Freezing of Gait Detection and Cueing System.

ABSTRACT: This paper presents a real-time wearable system designed to assist Parkinson's disease patients experiencing freezing of gait episodes. The system utilizes advanced machine learning models, including convolutional and recurrent neural networks, enhanced with past sample data preprocessing to achieve high accuracy, efficiency, and robustness. By continuously monitoring gait patterns, the system provides timely interventions, improving mobility and reducing the impact of freezing episodes. This paper explores the implementation of a CNN+RNN+PS machine learning model on a microcontroller-based device. The device operates at a real-time processing rate of 40 Hz and is deployed in practical settings to provide 'on demand' vibratory stimulation to patients. This paper examines the system's abili

SUBMITTER: Slemensek J 

PROVIDER: S-EPMC11505507 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature