Modeling and simulating electronic textile applications

Abstract

This paper describes our design of a simulation environment for electronic textiles (e-textiles) and our experiences with that environment. This simulation environment, based upon Ptolemy II, enables us to model a diverse range of areas related to the design of electronic textiles, including the physical environment they will be used in, the behavior of the sensors incorporated into the fabric, the on-fabric network, the power consumption of the system, and the execution of the application and system software. This paper focuses on two aspects of the system, modeling the motion of a person wearing the e-textile and modeling the effect of faults in the e-textile system. To partially validate this environment, we compare simulation results against results from two different physical prototypes, a large-scale acoustic beamformer and a pair of shape-sensing pants.

Recognizing Upper Body Postures using Textile Strain Sensors

“…Dunne et al [3] measured shoulder and neck movements with a textile piezo-resistive pressure sensor. In [8] a glove was realized using piezoelectric film sensors. Fiberoptics sensors were used in a pressure suit [2] and to measure the seated spinal posture [4].…”

Infrastructure and Reliability Analysis of Electric Networks for E-Textiles

“…To compare FPN with conventional fabric electric networks presented in [21] and [40], we set the nodes in four configurations, as shown in Fig. 9(a) Fig.…”

“…The majority of e-textiles prototypes and other research papers assume that power consuming elements obtain energy from a fixed battery, which is separated from the consuming elements [21], [40], [47]. Their implementations are based on "fixed" electric networks in which the interconnecting relations between the PCNs and the batteries are "one and only one."…”

“…As a novel kind of computing substrates, e-textiles represent an extreme form of distributed computing [21], [40]. The major features of fabric power networks include the following.…”

A Model for African Fabrics Analysis and Recognition

“…Though, colour histogram is robust to background complications and independent of image size and orientation, its sparsely nature makes it sensitive to noise. Fourier analysis and neural [15] was described, but the design had a greater dependence on physical locality of computation. A worked on retrieval of Songlet patterns based on their shapes using geometric shape descriptors from gradient edge detector was discussed in [9], while in [21], image processing techniques were used to determine the amount of fuzz value on the fabric surface and artificial neural network and regression analysis methods applied to predict the fuzz on the fabric surface to prevent defects on fabrics.…”