Regression models for estimating gait parameters using inertial sensors

Abstract

Advanced mathematical models are now widely used in medical applications for diagnosis, prognosis, and prevention of diseases. This work looks at the application of advanced regression models for estimating key foot parameters in falls prevention research. Falls is a serious issue for the rapidly increasing elderly demographic. We propose to investigate the notion of falls prediction through the use of portable, light weight, easy to use and inexpensive sensors along with advanced computational intelligence estimation models. This study compares two mathematical models namely the Generalized Regression Neural Networks (GRNN), and the Support Vector Machine (SVM) to estimate the key gait para..