Inferring Complex Textile Shape from an Integrated Carbon Black-infused Ecoflex-based Bend and Stretch Sensor Array

Abstract

We demonstrate how an array of custom-made strain and bend sensors could be integrated into a stretchable sleeve to infer the textile deformation. The angles and elongation measured by the sensors can be used by an optimisation-based algorithm to infer the textile geometrical model by minimising a loss function. We evaluated this on 4 shapes highlighting different body-part characteristics. We demonstrated that a 3.11 mm reconstruction error on complex geometries can be reduced up to 0.08 mm with the computation of angles. This proves the potential of the proposed prototype for capturing the shape of a body parts, muscle density measurement, body shape acquisition, the fabrication of orthoses and prostheses, or to perform movement sensing for human activity recognition, where it could be included in sports leggings for biomechanical analysis, or in everyday garments for motion and gesture sensing.

Shape Self-Sensing With Mutual Inductance Sensor Array

“…Shape sensing is required for the soft robots to detect their configuration, which is used for control of displacement and interaction with their environment. Shape Shape self-sensing with mutual inductance sensor array M Soleimani 1 , G Dingley 1 , E Semaj 1 , M Petrou 1 M sensing systems existing in research literature primarily use a distribution of sensors to reconstruct the deformation, such as strain gauges [14], and accelerometers [15]. Either many sensors are used or the sparse nature of information from sensors limits the sensing methods.…”