Computational Design of Kinesthetic Garments

TL;DR

This paper introduces an optimization-based method for designing reinforcement patterns in kinesthetic garments, enabling tailored physical feedback for applications like rehabilitation and sports, with significant performance improvements.

Contribution

It formulates the reinforcement design as an on-body topology optimization problem, allowing continuous exploration of design variations and capturing complex cloth-body interactions.

Findings

Optimal designs achieve 2-3x energy density improvement.

Manufactured designs outperform baselines in user ratings.

Method effectively explores a range of reinforcement distributions.

Abstract

Kinesthetic garments provide physical feedback on body posture and motion through tailored distributions of reinforced material. Their ability to selectively stiffen a garment's response to specific motions makes them appealing for rehabilitation, sports, robotics, and many other application fields. However, finding designs that distribute a given amount of reinforcement material to maximally stiffen the response to specified motions is a challenging problem. In this work, we propose an optimization-driven approach for automated design of reinforcement patterns for kinesthetic garments. Our main contribution is to cast this design task as an on-body topology optimization problem. Our method allows designers to explore a continuous range of designs corresponding to various amounts of reinforcement coverage. Our model captures both tight contact and lift-off separation between cloth and body. We demonstrate our method on a variety of reinforcement design problems for different body sites and motions. Optimal designs lead to a two- to threefold improvement in performance in terms of energy density. A set of manufactured designs were consistently rated as providing more resistance than baselines in a comparative user study