Data-Driven Parametric Analysis of Personalized SMA-Based Ankle-Foot Orthosis Designs

This study presents a design and evaluation method for an innovative ankle-foot orthosis (AFO) using shape memory alloy (SMA) technology, aimed at addressing drop foot syndrome. The design leverages 3D scanning, CAD, parametric modeling defined by four adjustable parameters, finite element analysis, and an advanced framework that combines design of experiments (DoE) with support vector machines (SVMs). This hybrid method not only predicts AFO performance but also minimizes computational demands while ensuring thorough exploration of the design space. The approach shows promise for enhanced patient outcomes through tailored support, showcasing the potential of digital technologies in developing high-performance orthotic devices.