Community-Based and Everyday Life Gait Analysis: Approach to an Automatic Balance Assessment and Fall Risk Prediction in the Elderly

Abstract

This chapter discusses the potential of wearable technologies in predicting fall risks among older adults, a demographic susceptible to falls due to age-related walking ability decline. We aimed to explore the feasibility of portable body sensors, mobile apps, and smartwatches for real-time gait analysis in non-clinical, everyday settings. We used classification models like Random Forest, Support Vector Machine with a radial basis function kernel, and Logistic Regression to predict fall risks based on gait parameters. Notably, both Random Forest and Support Vector Machine models demonstrated over 72% accuracy, underscoring the critical role of feature selection and model choice in fall risk prediction. These technologies can enhance older adults’ quality of life by predicting fall risks. However, future developments should focus on technologies adapted to non-clinical environments, predictivity, and high-risk group usability. The integration of these features may enable more efficient fall risk assessment systems.