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.

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Aerodynamic drag force can account for up to 90% of the opposing force experienced by a cyclist. Therefore, aerodynamic testing and efficiency is a priority in cycling. An inexpensive method to optimize performance is required. In this study, we evaluate a novel indoor setup as a tool for aerodynamic pose training. The setup consists of a bike, indoor home trainer, camera, and wearable inertial motion sensors. A camera calculates frontal area of the cyclist and the trainer varies resistance to the cyclist by using this as an input. To guide a cyclist to assume an optimal pose, joint angles of the body are an objective metric. To track joint angles, two methods were evaluated: optical (RGB camera for the two-dimensional angles in sagittal plane of 6 joints), and inertial sensors (wearable sensors for three-dimensional angles of 13 joints). One (1) male amateur cyclist was instructed to recreate certain static and dynamic poses on the bike. The inertial sensors provide excellent results (absolute error = 0.28?) for knee joint. Based on linear regression analysis, frontal area can be best predicted (correlation 0.4) by chest anterior/posterior tilt, pelvis left/right rotation, neck flexion/extension, chest left/right rotation, and chest left/right lateral tilt (p 0.01)..

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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.

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With the worldwide spread of the COVID-19 virus in early 2020, shortages of surgical masks and filtering facepiece respirator (FFR) masks became a critical problem. European governments recommended that civilians should not use these masks so that the shortages in the hospitals would be minimised. In Europe, civilians were instead advised to wear community face coverings. In June 2020, the European Committee for Standardisation (CEN) published CWA 17553:2020 [1–3] which formalised minimum requirements, methods of testing and use of community face coverings. The CWA 17553 is presently only a recommendation, and not an official standard such as the EN14683 standard for surgical masks or the EN149 standard for filtering facepiece respirators. Because there are different performance requirements for these three different classes of masks, it makes comparing their performance challenging. In this work, we perform particulate filtration efficiency measurement, total inward leakage measurement and breathability measurement on a range of surgical masks, filtering facepiece respirators and community face coverings. This analysis provides a useful comparison between material performance and the effectiveness of a mask’s design which is manufactured from this material.

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Gaining aerodynamic advantage is crucial in cycling, wherein drag force can account for 90% of resistance to a cyclist. The interaction of wind with the moving human and cycling equipment also contributes to drag. Testing equipment for aerodynamic properties in a wind tunnel is of interest to manufacturers in the sports industry. However, a protocol that needs to depend on the ability of a human to accurately recreate desired poses and movements multiple times may be cumbersome during testing. Additionally, a stationary mannequin does not accurately represent real-life cycling, where pedaling is a constant. Hence, a moving mannequin is suited for such applications. In this contribution, we demonstrate the development of a 3D printed articulating cycling mannequin. A cyclist was scanned using a structured light 3D scanner. Our goal was to develop an articulating mannequin with pedalling legs and a detachable upper-body to recreate two common cycling poses - time-trial and regular. In this contribution, we describe the full development of the mannequin in time trial pose. This approach outlines a low-cost method to developing mannequins for testing applications in sports. @en

Aerodynamic drag force and projected frontal area (A) are commonly used indicators of aerodynamic cycling efficiency. This study investigated the accuracy of estimating these quantities using easy-to-acquire anthropometric and pose measures. In the first part, computational fluid dynamics (CFD) drag force calculations and A (m²) values from photogrammetry methods were compared using predicted 3D cycling models for 10 male amateur cyclists. The shape of the 3D models was predicted using anthropometric measures. Subsequently, the models were reposed from a standing to a cycling pose using joint angle data from an optical motion capture (mocap) system. In the second part, a linear regression analysis was performed to predict A using 26 anthropometric measures combined with joint angle data from two sources (optical and inertial mocap, separately). Drag calculations were strongly correlated with benchmark projected frontal area (coefficient of determination R² = 0.72). A can accurately be predicted using anthropometric data and joint angles from optical mocap (root mean square error (RMSE) = 0.037 m²) or inertial mocap (RMSE = 0.032 m²). This study showed that aerodynamic efficiency can be predicted using anthropometric and joint angle data from commercially available, inexpensive posture tracking methods. The practical relevance for cyclists is to quantify and train posture during cycling for improving aerodynamic efficiency and hence performance.

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