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As an emerging technology, smart textiles have attracted attention for rehabilitation purposes or to monitor heart rate, blood pressure, breathing rate, body posture, as well as limb movements. Traditional rigid sensors do not always provide the desired level of comfort, flexibility, and adaptability. To improve this, recent research focuses on the development of textile-based sensors. In this study, knitted strain sensors that are linear up to 40% strain with a sensitivity of 1.19 and a low hysteresis characteristic were integrated into different versions of wearable finger sensors for rehabilitation purposes. The results showed that the different finger sensor versions have accurate responses to different angles of the index finger at relaxation, 45° and 90°. Additionally, the effect of spacer layer thickness between the finger and sensor was investigated. [ABSTRACT FROM AUTHOR]

In recent years, knitted strain sensors have been developed that aim to achieve reliable sensing and high wearability, but they are associated with difficulties due to high hysteresis and low gauge factor (GF) values. This study investigated the electromechanical performance of the weft-knitted strain sensors with a systematic approach to achieve reliable knitted sensors. For two elastic yarn types, six conductive yarns with different resistivities, the knitting density as well as the number of conductive courses were considered as variables in the study. We focused on the 1 × 1 rib structure and in the sensing areas co-knit the conductive and elastic yarns and observed that positioning the conductive yarns at the inside was crucial for obtaining sensors with low hysteresis values. We show that using this technique and varying the knitting density, linear sensors with a working range up to 40% with low hysteresis can be obtained. In addition, using this technique and varying the knitting density, linear sensors with a working range up to 40% strain, hysteresis values as low as 0.03, and GFs varying between 0 and 1.19 can be achieved. [ABSTRACT FROM AUTHOR]

Cooling vests containing phase change materials (PCMs) are used to reduce heat stress in hot environments and maintain the body core temperature within a safe range. The performance of such cooling vests depends in a complicated way on the PCM material and mass, the insulation value of the clothing layers and heat loss to the environment. Conventionally, these performance parameters are evaluated experimentally or using a numerical model, both of which do need a certain amount of evaluation time. The objective of this paper is to develop a transient heat transfer model which includes metabolic heat production in the human body, as well as clothing and PCM layers and radiation to the environment but which is presented as a series of closed-form equations that can be evaluated without the need of a numerical solver. We present solutions for the body and PCM temperature as well as for the heat flux, cooling power and cooling duration. The model equations are validated by comparing them with experiments of ice PCM packs on a hotplate, as well as with published experimental and numerical data for the core temperature, heat flux and percentage of environmental heat loss using a Glauber salt type of PCM. Both the hotplate experiments and the model predictions show that the cooling power during PCM melting drops from about 70 to 32 W for increasing insulation layer thicknesses. In addition, the model is seen to compare well with experimental and simulation data in the literature. In a parametric study, we show how the equations can be used to evaluate the effects of PCM melting temperature and PCM thickness on cooling performance. The paper, therefore, can be considered as a practical means to help select the best cooling vest configuration for workers in a hot and humid environment. [ABSTRACT FROM AUTHOR]

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we investigated the possibility to extract these statistics from acceleration sensor data generated by a previously developed sensor garment. We used deep learning-based models to recognize five football-related activities (jogging, sprinting, passing, shooting and jumping) in an accurate, robust, and fast manner. A combination of convolutional (CNN) layers followed by recurrent (bidirectional) LSTM layers achieved up to 98.3% of accuracy. Our results showed that deep learning models performed better in evaluation time and prediction accuracy than traditional machine learning algorithms. In addition to an increase in accuracy, the proposed deep learning architecture showed to be 2.7 to 3.4 times faster in evaluation time than traditional machine learning methods. This demonstrated that deep learning models are accurate as well as time-efficient and are thus highly suitable for cost-effective, fast, and accurate human activity recognition tasks. [ABSTRACT FROM AUTHOR]

By embedding conductive yarns in, or onto, knitted textile fabrics, simple but robust stretch sensor garments can be manufactured. In that way resistance based sensors can be fully integrated in textiles without compromising wearing comfort, stretchiness, washability, and ease of use in daily life. The many studies on such textile strain sensors that have been published in recent years show that these sensors work in principle, but closer inspection reveals that many of them still have severe practical limitations like a too narrow working range, lack of sensitivity, and undesired time-dependent and hysteresis effects. For those that intend to use this technology it is difficult to determine which manufacturing parameters, shape, stitch type, and materials to apply to realize a functional sensor for a given application. This paper therefore aims to serve as a guideline for the fashion designers, electronic engineers, textile researchers, movement scientists, and human-computer interaction specialists planning to create stretch sensor garments. The paper is limited to textile based sensors that can be constructed using commercially available conductive yarns and existing knitting and embroidery equipment. Within this subtopic, relevant literature is discussed, and a detailed quantitative comparison is provided focusing on sensor characteristics like the gauge factor, working range, and hysteresis.