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This paper studies the influence of fabric's structure on the thermal and moisture management properties of knitted fabrics made of two types of yarns with thermo-regulating effect: Coolmax ® and Outlast ®. The main purpose of this study was the selection of the most adequate fabric, to be used in summer and winter sportswear. The results demonstrated that some properties, such as, thermal properties, diffusion ability, air and water vapor permeability are influenced by both raw material type and knitted structure parameters. Wicking ability is influenced to a greater extent by the knitted structure, while the drying ability is primarily determined by raw material and to a lesser extent by the knitted structure parameters. Outlast ® fabrics are preferred candidates for warmer climate sportswear, particularly due to their lower thermal resistance, higher thermal conductivity and absorptivity, air and water vapor permeability. When considering sportswear for colder weather, Coolmax ® based structures seem to be the best choice. These findings are an important tool in the design of a sportswear product tailored to the different body areas thermal and moisture management requirements.

This paper gives an overview of technologies and results of integration and test of textile integrated sensors and electrodes for monitoring of biosignals (electrocardiographic -ECG and electromyographic -EMG), breathing and moisture. Using a seamless jacquard knitting machine, it is possible to integrate these sensors and electrodes directly into the fabrics, which can then be used in clothing for monitoring of elderly people, in sports or in hazardous occupations. The total integration of the sensing elements and connections into the garment presents great advantages in physical as well as psychological comfort of the user. It has been shown that the measurements are of adequate quality for most of the applications. In some cases, as is the case of ECG and EMG, signals acquired are similar to those obtained using conventional electrodes.

Indoor Positioning Systems (IPSs) for emergency responders is a challenging field attracting researchers worldwide. When compared with traditional indoor positioning solutions, the IPSs for emergency responders stand out as they have to operate in harsh and unstructured environments. From the various technologies available for the localization process, ultra-wide band (UWB) is a promising technology for such systems due to its robust signaling in harsh environments, through-wall propagation and high-resolution ranging. However, during emergency responders’ missions, the availability of UWB signals is generally low (the nodes have to be deployed as the emergency responders enter a building) and can be affected by the non-line-of-sight (NLOS) conditions. In this paper, the performance of four typical distance-based positioning algorithms (Analytical, Least Squares, Taylor Series, and Extended Kalman Filter methods) with only three ranging measurements is assessed based on a COTS UWB transceiver. These algorithms are compared based on accuracy, precision and root mean square error (RMSE). The algorithms were evaluated under two environments with different propagation conditions (an atrium and a lab), for static and mobile devices, and under the human body’s influence. A NLOS identification and error mitigation algorithm was also used to improve the ranging measurements. The results show that the Extended Kalman Filter outperforms the other algorithms in almost every scenario, but it is affected by the low measurement rate of the UWB system.

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