Research Article
Lessons Learned in Developing Sensorised Textiles to Capture Body Shapes
@INPROCEEDINGS{10.1007/978-3-030-99194-4_23, author={Leonardo A. Garc\^{\i}a-Garc\^{\i}a and George Valsamakis and Niko M\'{y}nzenrieder and Daniel Roggen}, title={Lessons Learned in Developing Sensorised Textiles to Capture Body Shapes}, proceedings={Pervasive Computing Technologies for Healthcare. 15th EAI International Conference, Pervasive Health 2021, Virtual Event, December 6-8, 2021, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2022}, month={3}, keywords={Sensing sleeve Smart textile Body monitoring Shape reconstruction Flexible sensors}, doi={10.1007/978-3-030-99194-4_23} }- Leonardo A. García-García
George Valsamakis
Niko Münzenrieder
Daniel Roggen
Year: 2022
Lessons Learned in Developing Sensorised Textiles to Capture Body Shapes
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-030-99194-4_23
Abstract
Motivated by the need to replace plaster casts or image acquisition approaches to capture body shapes to create orthoses, we explored the feasibility of using smart textile sleeve enhanced with arrays of stretch and bend sensors. The sensors’ data is interpreted by an ad-hoc optimisation-based shape inference algorithm to come up with a digitised 3D model of the body part around which the sleeve is worn. This paper summarises the state of the art in the field, before illustrating the approach we followed and lesson’s learned in developing smart textile sleeves and the associated data processing algorithms. The unique approach we followed was to realise from the ground up the sensing elements, their integration into a textile, and the associated data processing. In the process, we developed a technology to create stretch and bend sensing elements using carbon black and ecoflex, improving curvature detection; we also found ways to interconnect large arrays of such sensors, digitise their data, and developed several mathematical optimisation models for the inference of the sleeve shape from the sensor readings.
