Graduated compression is widely used for medical application to prevent perioperative venous thromboembolism, but other applications such as sportswear can potentially also benefit from it. A tight-fit cycling shirt meant to ensure the correct position during cycling and prevent injuries was designed. The aim of this study was to improve garment pattern design from the aspect of clothing pressure for providing support and enhancing comfort to the user. This paper investigates the suitability of pressure maps from 3D fashion design software CLO 3D for design and in particular its capability to discriminate between various materials and cycling postures. Moreover, the impact of the mechanical properties of fabric was analyzed. In particular, virtual prototyping tool CLO 3D and pressure mapping were employed to achieve the required graduated compression while ensuring fit and comfort. Pattern adjustments were iteratively performed until stress, strain, and pressure maps showed adequate fit and pressure of the cycling garment on the virtual cyclist in static and dynamic cycling positions. The impact of fabric types on garment fit has been shown by generating the stress, strain, and pressure maps with a virtual simulation. It was found that the visualized pressure on the human body model shows distributions that are related to contact between body and garment, and large compression stresses occur in the lower parts of the two shirts. Evident garment deformation was shown at hip level, upper arm, lower front side seam, and front neck, which can reduce garment wear comfort and freedom of movement. The output was found to be sufficiently accurate to optimize the garments based on material and cycling posture.

Protective and sport clothing is governed by protection requirements, performance, and comfort of the user. The comfort and impact performance of protective and sport clothing are typically subjectively measured, and this is a multifactorial and dynamic process. The aim of this review paper is to review the contemporary methodologies and approaches for measuring ergonomic wear comfort, including objective and subjective techniques. Special emphasis is given to the discussion of different methods, such as objective techniques, subjective techniques, and a combination of techniques, as well as a new biomechanical approach called modeling of skin. Literature indicates that there are four main techniques to measure wear comfort: subjective evaluation, objective measurements, a combination of subjective and objective techniques, and computer modeling of human–textile interaction. In objective measurement methods, the repeatability of results is excellent, and quantified results are obtained, but in some cases, such quantified results are quite different from the real perception of human comfort. Studies indicate that subjective analysis of comfort is less reliable than objective analysis because human subjects vary among themselves. Therefore, it can be concluded that a combination of objective and subjective measuring techniques could be the valid approach to model the comfort of textile materials.

The aim of this study is to develop a mathematical model for the prediction of compression pressure based on fabric parameters, such as engineering stress, engineering strain and engineering modulus of elasticity. Four knitted compression fabrics with different fibrous compositions and knit structures were used. Rectangular-cut strips were employed for the force–elongation characterization of the fabrics. The experimental pressure values between the fabric and rigid cylinder were assessed using a Picopress pressure measuring device. The mechanical and physical parameters of the fabric that influence the interface pressure, such as strain, elasticity modulus/stress and thickness, were determined and integrated into Laplace’s law. A good correlation was observed between the experimental and calculated pressure values for all combinations of fabrics, mounted with variable tension on the cylinder. Over the considered range of pressures, the difference between the two datasets was generally less than 0.5 mmHg. The effect of washing after five, ten and fifteen washing cycles on the fabric–cylinder interface pressure was found to be significant.

This research focused on the investigation of the thermophysiological comfort properties of four selected knitted fabrics of different fiber blend ratios suitable for cycling wear. Comfort-related properties of the fabrics were determined and compared including air permeability, moisture management properties, drying time, thermal conductivity, and water vapor permeability. For those comfort properties of the fabric to be correlated, fabric structural properties, fabric density, fabric weight, and fabric thickness have been considered. Suited fabrics should have good air permeability, thermal conductivity, moisture management properties, and a short drying time. According to the measurement results, the fabric polyamide/elasane (58/42 PA6.6/EL) with good air permeability, thermal conductivity, moisture management properties, and short drying time was more suited for summer cycling clothing. Furthermore, this paper provides a new understanding of considerations that are needed for several end uses involving specific activity levels.

Fabric stretch sensors are available as planar fabrics, but their reliability and reproducibility are low. To find a good working setup for use in an elastic sports garment, the design of such sensors must be optimized. The main purpose of this study was to develop resistive strain sensors from stretchable conductive fabric and investigating the influence of stretchability on conductivity/resistivity. The influence of using the sensor in a sweat rich environment was also determined, in order to evaluate the potential use of the sensor in sporting garments. The sensor resistivity performance was analyzed for its sensitivity, working range, and repeatability and it was determined what makes the sensitivity when elongated or stretched. The resistivity was found to decrease with elongation if no sweat is present, this can be due to molecular rearrangement and a higher degree of orientation that improves the conductivity of a material. The result from this finding also shows that for wearable applications the commercial EeonTex^TM conductive stretchable fabric did not show a considerable resistivity increase, nor a good sensitivity. The sensitivity of the sensor was between 0.97 and 1.28 and varies with different elongation %. This may be due to the mechanical deformation characteristics of knitted samples that lead to changes in conductivity. We advise that the testing performed in this paper is done by default on new stretch sensitive textile materials, so practical use of the material can be correctly estimated.

Improper cycling posture is linked to a variety of spinal musculoskeletal diseases, including structural malformation of the spine and back discomfort. This paper presents a novel smart shirt integrated tri-axial gyroscope and accelerometer that can detect postural variation in terms of spinal curvature changes. To provide accurate feedback to the wearer and improve the wearer’s correct movement, the garment is able to recognize trunk body posture. The gyroscope/accelerometer was placed around the upper and mid trunk of the user to record tri-axial angular velocity data. The device can also be used to help determine the trunk bending angle and monitor body postures in order to improve optimal orientation and position. The garment enables continuous measurement in the field at high sample rates (50 Hz), and the sensor has a large measurement range (16 g, 2000°/s). As electronic components are non-washable, instead of encapsulating them, a detachable module was created. In this, magnets are embedded in the jersey, and allow the positioning and removal of the sensor. The test results show that the average trunk-bending angle was 21.5°, and 99 percent of the observed angle fell within the standard (ranging from 8° to 35°). The findings demonstrate the feasibility of employing the smart shirt sensor to estimate trunk motions in the field on a regular basis.