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.@en

Boron-doped diamond (BDD) is a popular material for electrodes and it exhibits metal-like conductivity when a sufficient quantity of boron atoms is incorporated in the diamond lattice. BDD has distinct advantages over alternative electrode materials. It has diamond’s chemical stability and biocompatibility in conjunction with excellent electrochemical characteristics including large potential window and low background current. Thus, BDD has great potential as an electrode material in electrochemical sensors for the detection of pharmaceutical compounds. Of particular interest is the monitoring of the antidepressants venlafaxine (VF) and desvenlafaxine (DVF). These pharmaceuticals are prescribed to those suffering from major depressive disorder, generalized anxiety disorder, panic disorder and/or social anxiety disorder. However, they can have adverse effects on the health and behaviour of aquatic life, and harmful quantities have already been detected in nature. Monitoring of DVF and VF in a patient’s blood and urine is required to ensure correct dosage levels, which in turn could mitigate environmental pollution. An electrochemical sensor with BDD electrodes would be well-suited for this application. In this research, two distinct BDD electrode materials were used for the electrochemical detection of VF and DVF. For initial experimentation, a robust and well-established free-standing BDD electrode type was utilized in a voltammetric study. Optimized detection conditions were achieved on hydrogen-terminated BDD for DVF and oxygen-terminated BDD for VF in a 0.1 M H2SO4 solution (pH 0.6), yielding limits of detection (LOD) of 0.31 µM and 0.17 µM and limits of quantification (LOQ) of 0.94 µM and 0.57 µM for VF and DVF, respectively. The scan rate study demonstrated that the oxidation reactions for both compounds are diffusion controlled. VF and DVF have excellent repeatability in the presence of several interfering compounds, such as inorganic ions, sucrose, glucose, and dopamine. To assess the suitability of the detection method, electroanalysis of VF and DVF in synthetic human serum, synthetic urine, and river water was conducted. Besides, a commercially available BDD electrode chip was employed for VF and DVF detection under optimized conditions. The measurement results of the different BDD electrodes were compared; in particular, when the less robust commercial electrode chip was used, larger values of LOD (1.9 µM and 8.0 µM) and LOQ (5.8 µM and 24.1 µM) were reached for VF and DVF, respectively. The fabrication of analogue BDD-based electrochemical sensors by utilizing direct inkjet printing of diamond nanoparticles on silicon substrates was evaluated in parallel. Various electrode designs were successfully printed, however subsequent chemical vapor deposition of thin-film BDD did not satisfy the required electrode quality. Notwithstanding, the use of a BDD electrode allows for the creation of a modification-free and promising practical method for VF and DVF detection.

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).. @en

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).. @en