Deformation Capture via Soft and Stretchable Sensor Arrays

TL;DR

This paper introduces a low-cost, flexible sensor fabrication method and a data-driven approach to accurately capture 3D surface deformations without line-of-sight, enabling wearable sensors for motion tracking.

Contribution

It presents a novel fabrication process for stretchable sensors and a deep learning-based method for reconstructing 3D deformations from area measurements.

Findings

High accuracy in deformation reconstruction demonstrated

Sensors operate effectively under occlusion and partial visibility

Prototype wearable sensors successfully track limb movements

Abstract

We propose a hardware and software pipeline to fabricate flexible wearable sensors and use them to capture deformations without line of sight. Our first contribution is a low-cost fabrication pipeline to embed multiple aligned conductive layers with complex geometries into silicone compounds. Overlapping conductive areas from separate layers form local capacitors that measure dense area changes. Contrary to existing fabrication methods, the proposed technique only requires hardware that is readily available in modern fablabs. While area measurements alone are not enough to reconstruct the full 3D deformation of a surface, they become sufficient when paired with a data-driven prior. A novel semi-automatic tracking algorithm, based on an elastic surface geometry deformation, allows to capture ground-truth data with an optical mocap system, even under heavy occlusions or partially unobservable markers. The resulting dataset is used to train a regressor based on deep neural networks, directly mapping the area readings to global positions of surface vertices. We demonstrate the flexibility and accuracy of the proposed hardware and software in a series of controlled experiments, and design a prototype of wearable wrist, elbow and biceps sensors, which do not require line-of-sight and can be worn below regular clothing.