The future design direction of smart clothing development

Literature indicates that Smart Clothing applications, the next generation of clothing and electronic products, have been struggling to enter the mass market because the consumers’ latent needs have not been recognised. Moreover, the design direction of Smart Clothes remains unclear and unfocused. Nevertheless, a clear design direction is necessary for all product development. Therefore, this research aims to identify the design directions of the emerging Smart Clothes industry by conducting a questionnaire survey and focus groups with its major design contributors. The results reveal that the current strategy of embedding a wide range of electronic functions in a garment is not suitable. This is primarily because it does not match the users’ requirements, purchasing criteria and lifestyle. The results highlight the respondents’ preference for personal healthcare and sportswear applications that suit their lifestyle, are aesthetically attractive, and provide a practical function

Creating a global vision for sustainable fashion

Textiles, the fastest growing sector in household waste, have created an exponential rise in the export of second hand clothes (SHC) to overseas markets such as Kenya and Tanzania. Despite the few advantages for the destination markets (eg, enterprise opportunities), this has exasperated a difficult situation for domestic production. Increased cheap imports from Asia have also led to decline in SHC markets, resulting in increased land filling and the associated environmental impacts. Our research proposes remanufacturing fashion from the unwanted SHC, embellishing using local (destination market) craft/design. From literature review conducted, reuse and remanufacture of clothing causes the least impact on energy use and appears to be the most environmentally and socially friendly approach to sustainability efforts. Remanufacture of clothing is currently practiced at niche market levels, for it to have a broader impact; it needs to gain entry into the mass-market retail arena. In the mass market arena, the apparel value chain is organized around several parts with a marketing network at the retail level. Lead firms predominantly construct these value chains, are predominantly located in developed countries, and may be large retailers and brand-name firms, playing a significant role in specifying what is to be produced, how, and by whom. Our goal is to understand how designers, manufacturers and retailers may work together in a remanufacturing process. We present findings from interviews with Tanzanian second hand clothes retailers and artisans, UK fashion remanufacturers and retailers. We discuss the implications on the fashion design process and propose a new product development method for sustainable consumption of fashion. We conclude by reflecting on potential mechanisms of the supply chain integration and how the large multinationals may become engaged. Key words: remanufacturing, design process, supply chain, second hand clothe

Texture wear analysis in textile floor coverings by using depth information

Considerable industrial and academic interest is addressed to automate the quality inspection of textile floor coverings, mostly using intensity images. Recently, the use of depth information has been explored to better capture the 3D structure of the surface. In this paper, we present a comparison of features extracted from three texture analysis techniques. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show that the use of Local Binary Patterns techniques result in a better ranking of the wear labels as well as in a higher amount of discrimination between features related to consecutive degrees of wear

Weaving Lighthouses and Stitching Stories: Blind and Visually Impaired People Designing E-textiles

We describe our experience of working with blind and visually impaired people to create interactive art objects that are personal to them, through a participatory making process using electronic textiles (e-textiles) and hands-on crafting techniques. The research addresses both the practical considerations about how to structure hands-on making workshops in a way which is accessible to participants of varying experience and abilities, and how effective the approach was in enabling participants to tell their own stories and feel in control of the design and making process. The results of our analysis is the offering of insights in how to run e-textile making sessions in such a way for them to be more accessible and inclusive to a wider community of participants

Feature extraction of the wear label of carpets by using a novel 3D scanner

In the textile industry, the quality of carpets is still determined through visual assessment by human experts. Human assessment is somewhat subjective, so there is a need for a more objective assessment which yields to automated systems. However, existing computer models are at this moment not yet capable of matching the human expertise. Most attempts at automated assessment have focused on image analysis of two dimensional images of worn carpet. These do not adequately capture the three dimensional structure of the carpet that is also evaluated by the experts and the image processing is very dependent on the lighting conditions. One previous attempt however used a laser scanner to obtain three dimensional images of the carpet and process them for carpet assessment. This paper describes the development of a new scanner to acquire wear label characteristics in three dimensions based on a structured light pattern. Now an appropriate technique based on the local binary patterns (LBP) and the Kullback-Leibler divergence has been developed. We show that the new laser scanning system is less dependent on the lighting conditions and color of the carpet and obtains data points on a structured grid instead of sparse points. The new system is also more than five times cheaper, scans more than seven times faster and is specifically designed for scanning carpets instead of 3D objects. Previous attempts to classify the carpet wear were based on several extracted features. Only one of them - the height difference between worn and unworn part - showed a good correlation of 0.70 with the carpet wear label. However, experiments demonstrate that our approach - using the LBP technique - gives rise to promising results, with correlation factors from 0.89 to 0.99 between the Kullback-Leibler divergence and quality labels. This new laser scanner system is a significant step forward in the automated assessment of carpet wear using 3D images

GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures

Chi-square histogram distance is one of the distance measures that can be used to find dissimilarity between two histograms. Motivated by the fact that texture discrimination by human vision system is based on second-order statistics, we make use of histogram of gray-level co-occurrence matrix (GLCM) that is based on second-order statistics and propose a new machine vision algorithm for automatic defect detection on patterned textures. Input defective images are split into several periodic blocks and GLCMs are computed after quantizing the gray levels from 0-255 to 0-63 to keep the size of GLCM compact and to reduce computation time. Dissimilarity matrix derived from chi-square distances of the GLCMs is subjected to hierarchical clustering to automatically identify defective and defect-free blocks. Effectiveness of the proposed method is demonstrated through experiments on defective real-fabric images of 2 major wallpaper groups (pmm and p4m groups).Comment: IJCVR, Vol. 2, No. 4, 2011, pp. 302-31

A 3D descriptor to detect task-oriented grasping points in clothing

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor. In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.Peer ReviewedPostprint (author's final draft

Measuring hairiness in carpets by using surface metrology

Recently, an automatic system for grading appearance retention in carpets using our own scanner and image analysis techniques was proposed. A system for carpets with low pile construction and without color patterns has been developed. Appearance changes in carpets with high pile construction were still not well detected. We present an approach based on surface metrology that extract information given by the hairs on the carpet surface. These features are complementary to the texture features previously explored. By combining both features, we expand the use of the automatic grading system including some carpets types with high pile construction