Wearable sensors and machine learning fusion-based fall risk prediction in covert cerebral small vessel disease.

ABSTRACT:

Background

Fall risk prediction is crucial for preventing falls in patients with cerebral small vessel disease (CSVD), especially for those with gait disturbances. However, research in this area is limited, particularly in the early, asymptomatic phase. Wearable sensors offer an objective method for gait assessment. This study integrating wearable sensors and machine learning, aimed to predict fall risk in patients with covert CSVD.

Methods

We employed soft robotic exoskeleton (SRE) to acquire gait characteristics and surface electromyography (sEMG) system to collect sEMG features, constructing three datasets: gait-only, sEMG-only, and their combination. Using Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Neural Network (NN) al

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11879974 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature