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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.

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This study investigated the potential possibilities for obtaining textile transmission lines by incorporating conductive yarns into fabrics through a hot air welding process. Hot air sealing for obtaining textile transmission line was conducted using 100 % PES woven fabric, GoreTex ® waterproof welding tape and seven different conductive yarn types, in order to form different textile transmission lines. By manufacturing using a hot air seam-sealing machine different welding parameters like welding temperature, pressure and velocity were chosen in order to find an optimal welding process for the selected fabric samples. The effects of welding parameters were examined on the electrical properties of the textile transmission lines in terms of conductivity and signal-transferring capability. Besides, the bending properties and morphologies of the welded textile transmission lines were also characterized and subjective evaluations of the appearances of textile transmission lines like puckering and the visual appearances of the surface sides of the welded textile transmission lines. The results based on conductivity and signal transferring capabilities were really promising for the manufacturing of etextile transmission lines via hot air welding technology. Moreover, the results based on bending properties showed that the lower the welding parameters the less rigid the hot air welded textile transmission lines became after welding all the used conductive yarns. Further, suitable combinations of welding parameters with the used components of textile transmission assured suitable visual appearances of the welded textile transmission lines. In this respect this research work offers a usage for hot air welding technology regarding the formations of textile transmission lines which are reliable and durable in functionality while still preserving the textiles' aspects.

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.