Grasp classification using data gloves can enable therapists to monitor patients efficiently by providing concise information about the activities performed by these patients. Although, classical machine learning algorithms have been applied in grasp classification, they require manual feature extraction to achieve high accuracy. In contrast, convolutional neural networks (CNNs) have outperformed popular machine learning algorithms in several classification scenarios because of their ability to extract features automatically from raw data. However, they have not been implemented on grasp classification using a data glove. In this study, we apply a CNN in grasp classification using a piezoresistive textile data glove knitted from conductive yarn and an elastomeric yarn. The data glove was used to collect data from five participants who grasped thirty objects each following Schlesinger's taxonomy. We investigate a CNN's performance in two scenarios where the validation objects are known and unknown. Our results show that a simple CNN architecture outperformed k-nn, Gaussian SVM, and Decision Tree algorithms in both scenarios in terms of the classification accuracy.
Weft knitted conductive fabrics can act as excellent textile strain sensors for human motion capture. The loop architecture dictates the overall electrical properties of weft knit strain sensors. Therefore, research into loop architecture is relevant for comprehensively investigating the design space of e-textile sensors. There are three main types of knit stitches, Knitted loop stitch, Miss stitch, and Tuck stitch. Nevertheless, most of the research into weft knit strain sensors has largely focused on fabrics with only knitted loop stitches. Miss and tuck stitches will affect the contact points in the sensor and, consequently, its piezoresistivity. Therefore, this paper investigates the impact of incorporating miss and tuck stitches on the piezoresistivity of a weft knit sensor. Particularly, the electromechanical models of a miss stitch and a tuck stitch in a weft knit sensor are proposed. These models were used in order to develop loop configurations of sensors that consist of various percentages of miss or tuck stitches. Subsequently, the developed loop configurations were simulated while using LTspice and MATLAB software; and, verified experimentally through a tensile test. The experimental results closely agree with the simulated results. Furthermore, the results reveal that increases in the percentage of tuck or miss stitches in weft knit sensor decrease the initial and average resistance of the sensor. In addition, it was observed that, although the piezoresistivity of a sensor with tuck or miss stitches is best characterised as a quadratic polynomial, increases in the percentage of tuck stitches in the sensor increase the linearity of the sensor’s piezoresistivity.
This paper presents significant advances in mycelium biofabrication using permanent knitted textile formwork and a new substrate formulation to dramatically improve the mechanical properties of mycelium-textile biocomposites suitable for large-scale components for use in construction. The paper outlines the biofabrication process, detailing the composition of mycocrete, a viscous mycelium substrate developed for use with permanent knitted formwork, and the injection process required to regulate the filling of slender tubes of fabric with mycocrete. The use of a permanent integrated knitted formwork shows promise as a composite system for use with mycelium to improve mechanical performance and enable complex shapes to be fabricated for lightweight construction. Results of mechanical testing show dramatic improvements in tensile, compressive and flexural strength and stiffness compared to conventional mycelium composites. The testing demonstrates the importance of both the mycocrete paste recipe and the knitted textile formwork. In addition, the paper highlights the advantages of the proposed biofabrication system with reference to the BioKnit prototype: a 1.8 m high freestanding arched dome composed of very slender biohybrid knit-mycelium tubes. This prototype demonstrates the opportunity to utilize the potential for lightweight construction and complex form offered by a textile formwork with low environmental impact mycelium biomaterials. The combination of textiles and mycelium present a compelling new class of textile biohybrid composite materials for new applications within the construction sector.
It is widely acknowledged that textile processing is increasingly unsustainable, for example textile dyeing is experiencing a rising use of water, leading to a scarcity of freshwater globally (Easton, 2009, p145). It is imperative to investigate alternative strategies to colouration and whilst there is no single resolution to the problem, using design intelligence from diverse design specialisms, in this instance knitted fabric design, can offer a realistic framework within which to develop solutions.Following established design methodologies, successful knit design requires knowledge of materials, process, technology and aesthetics which is utilised in unique combinations to create a specified product. Disrupting this approach to design through the application of innovative technologies or removing the concept of designing for a specified product, this unique body of knowledge can question wider societal problems, including textile coloration, and determine a range of solutions through knitted fabric design practice.The paper will report on the development of sustainable textile coloration through innovative lighting technologies. New research explores the breadth of colour gamut achievable with a limited palette of yarn (so minimising dyeing) when recognised optical effects, for example optical mixing, are observed in different lighting conditions. The iterative design/research methodology used exploits the materiality and structural knowledge inherent in knitted fabrics and allows the creation of unique fabrics, which would be unachievable in any other medium, to test ideas abductively. A feature of this methodology is an acceptance of unexpected outcomes that challenge the concept of designing for a specified product. The fabrics produced are not in themselves functional, except as a communication tool for the knowledge revealed through the design research.
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