  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
