Featured Application: Improving garment selection without manual measuring for made-to-order sports garments. As sports garments are stretchable, different sizing tables are used than for retail clothing. However, customers measuring themselves leads to errors and unsatisfaction, since these customized branded garments cannot be returned. Using fitting sets avoids this, but this is not always feasible, especially in an online retail environment. Therefore, this research aims to use descriptive measures—parameters that do not require manual measuring because they are readily known by heart by almost any customer—to predict users’ body measurements, which can, thus, be used by customers to determine the size of their sports garment from a sizing chart. To validate if these input measures are sufficient to predict the correct size, three prediction methods are used and compared with baseline manual measurements. The methods are: (i) clothing size predictions from shape models with descriptive measures as inputs, (ii) clothing size predictions from a regression analysis, and (iii) clothing size predictions from a shape model based on extensive 3D scanned measurements as input. The conclusion is that a regression algorithm with, as input variables, the straightforward demographics of age, gender, stature, and weight is more accurate than the algorithm with the same inputs but with a shape model behind it. Moreover, chest and hip circumferences have an intraclass correlation coefficient rating above 0.9 and are, thus, suited for online retail of stretchable garments, such as cycling clothes. As validated by end-users, the regression predictions are shown to agree with preferred garment sizes of the participants, within the natural variation of personal preferences.

The current COVID-19 pandemic has resulted in an immense and unforeseen increase in demand for personal protective equipment (PPE) for healthcare workers worldwide. Amongst other products, respirator masks are crucial to protect the users against transmission of the virus. Decontamination and reuse of the existing stock could be a solution to the shortage of new respirators. Based upon existing studies, it was found that (I) a solid quality control method is essential to test product reuse, (II) in-depth evaluation of the different parts of the filtering facepiece respirator (FFR) should be considered, and (III) communication of the reuse cycle is essential to take track of the amount of reuse, as this is limited to ensure quality. The goal of this paper is two-fold. First, we identify the impact of decontamination on the different parts of the FFRs and how the quality control should be performed. Two different types of FFRs are analysed within this paper, resulting in the recommendation of combining quantitative respirator mask fit testing with a thorough sensory evaluation of decontaminated FFRs to qualify them for reuse. Secondly, the possibilities of communication of this reuse to the eventual user are mapped through in-depth reasoning.

Driven by the competitive market that product designers face today, a growing interest emerges in exploring experiential material qualities to enhance product experience. The maturing of the research area calls for standardization to evolve to more streamlined and systematic approaches to conduct characterization experiments. To this aim, we conducted a literature review on 64 cases of experiential characterization studies in the materials and design domain. In this paper, we summarize the current state of the art, formulate an overview to facilitate systematic studies to explore experiential qualities of materials, and identify gaps or opportunities for further research. The presented learnings shed light on the following aspects used in materials experience studies: (i) variables, (ii) stimuli, (iii) interaction modalities, (iv) experimental set-up, (v) methods employed in the conducted studies, and (vi) respondents. Two important gaps were identified with regard to the physical material representations in an abstract form as a critical element for multimodal material characterization experiments, and to an integration of extensive user aspects beyond demographic variables to facilitate consumer segmentation. Additional future research suggestions were formulated, concerning within-material-class comparisons, complementary methods and experimental set-up, and the temporality of materials experience.