Motion-Driven Neural Optimizer for Prophylactic Braces Made by Distributed Microstructures

TL;DR

This paper introduces a neural network-based computational framework for designing personalized prophylactic braces by optimizing microstructure distribution to effectively prevent joint injuries, demonstrated on knee and ankle braces.

Contribution

It presents a novel differentiable end-to-end pipeline that optimizes microstructure distribution in braces using human motion data and biomechanical analysis.

Findings

Optimized braces reduce harmful joint movements in simulations.

Fabricated braces show effectiveness in physical tests.

The method enables personalized brace design based on biomechanical data.

Abstract

Joint injuries, and their long-term consequences, present a substantial global health burden. Wearable prophylactic braces are an attractive potential solution to reduce the incidence of joint injuries by limiting joint movements that are related to injury risk. Given human motion and ground reaction forces, we present a computational framework that enables the design of personalized braces by optimizing the distribution of microstructures and elasticity. As varied brace designs yield different reaction forces that influence kinematics and kinetics analysis outcomes, the optimization process is formulated as a differentiable end-to-end pipeline in which the design domain of microstructure distribution is parameterized onto a neural network. The optimized distribution of microstructures is obtained via a self-learning process to determine the network coefficients according to a carefully designed set of losses and the integrated biomechanical and physical analyses. Since knees and ankles are the most commonly injured joints, we demonstrate the effectiveness of our pipeline by designing, fabricating, and testing prophylactic braces for the knee and ankle to prevent potentially harmful joint movements.