This review summarizes the recent developments and importance of wearable electronic textiles in the past decade. Wearable electronic textiles are an emerging interdisciplinary research area that requires new design approaches. This challenging interdisciplinary research field brings together specialists in electronics, information technology, microsystems, and textiles to make an innovation in the development of wearable electronic products. Wearable electronic textiles play a key role among various technologies (clothing, communication, information, healthcare monitoring, military, sensors, magnetic shielding, etc.). In this review, applications of wearable electronic textiles are described, including an investigation of their fabrication techniques. This review highlights the basic processes, possible applications, and main materials to build wearable E‐textiles and combines the fundamentals of E‐textiles for the readers who have different backgrounds. Moreover, reliability, reusability, and efficiency of wearable electronic textiles are discussed together with the opportunities and drawbacks of the wearable E‐textiles that are addressed in this review article.

In this study, it was aimed to investigate the relationship between different knitted structures and some thermophysiological comfort parameters. Wetting, wicking and drying properties of single jersey, 1 Â 1 rib, 2 Â 2 rib and interlock knitted fabrics made out of acrylic yarns were studied and experimental wicking height, wicking weight, transfer wicking ratio, contact angle and WER (water evaporation rate) values were measured. Samples were produced in two different tightness values to obtain slack and tight fabrics for all structures. Some comfort-related parameters were correlated with structural parameters of fabrics such as fabric tightness factor, thickness, porosity, loop length and pore size etc. The statistical analysis results indicate that the effect of the knitted structure is significant for wicking height, wicking weight, contact angle values, transfer wicking ratios and WER values. Wicking height increases depending on knitted structures namely, single jersey, 1 Â 1 rib, interlock and 2 Â 2 rib, respectively. Slack fabrics have longer loop lengths with higher porosity values and higher pore sizes for all knitted structures. Slack structures of 2 Â 2 rib, 1 Â 1 rib, interlock and single jersey knits have higher transfer wicking ratios when compared with their tight structures. WER is inversely related with fabric thickness. It decreased with an increase of thickness due to increase of compactness and decrease of air space. All tight knitted structures have higher contact angles than their slack forms due to compactness of the surface.

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Polyurethane (PU)-polypyrrole (PPy) composite films and nanofibers were successfully prepared for the purpose of combining the properties of PU and PPy. Pyrrole (Py) monomer was polymerized and dispersed uniformly throughout the PU matrix by means of oxidative polymerization with cerium(IV) [ceric ammonium nitrate Ce(IV)] in dimethylformamide. Films and nanofibers were prepared with this solution. The effects of the PPy content on the thermal, mechanical, dielectric, and morphological properties of the composites were investigated with differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), Fourier transform infrared (FTIR)-attenuated total reflection (ATR) spectroscopy, dielectric spectrometry, and scanning electron microscopy. The Young's modulus and glass-transition temperatures of the composites exhibited an increasing trend with increases in the initially added amount of Py. The electrical conductivities of the composite films and nanofibers increased. The crystallinity of the composites were followed with DSC, the mechanical properties were followed with DMA, and the spectroscopic results were followed with FTIR-ATR spectroscopy. In the composite films, a new absorption band located at about 1650 cm À1 appeared, and its intensity improved with the addition of Py. The studied composites show potential for promising applications in advanced electronic devices.

In recent years, due to the widespread usage of various sensors action recognition is becoming more popular in many fields such as person surveillance, human-robot interaction etc. In this study, we aimed to develop an action recognition system by using only limited accelerometer and gyroscope data. Several deep learning methods like Convolutional Neural Network(CNN), Long-Short Term Memory (LSTM) with classical machine learning algorithms and their combinations were implemented and a performance analysis was carried out. Data balancing and data augmentation methods were applied and accuracy rates were increased noticeably. We achieved new stateof-the-art result on the UCI HAR dataset by 97.4% accuracy rate with using 3 layer LSTM model. Also, we implemented same model on collected dataset (ETEXWELD) and 99.0% accuracy rate was obtained which means a solid contribution. Moreover, the performance analysis is not only based on accuracy results, but also includes precision, recall and f1-score metrics. Additionally, a real-time application was developed by using 3 layer LSTM network for evaluating how the best model classifies activities robustly.