Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential in sports. This comprehensive review explores the measurement and monitoring of athletic performance, injury prevention, rehabilitation, and overall performance optimization using body wearable sensors. By analyzing wearables’ structure, research articles across various sports, and commercial sensors, the review provides a thorough analysis of wearable sensors in sports. Its findings benefit athletes, coaches, healthcare professionals, conditioners, managers, and researchers, offering a detailed summary of wearable technology in sports. The review is expected to contribute to future advancements in wearable sensors and biometric data analysis, ultimately improving sports performance. Limitations such as privacy concerns, accuracy issues, and costs are acknowledged, stressing the need for legal regulations, ethical principles, and technical measures for safe and fair use. The importance of personalized devices and further research on athlete comfort and performance impact is emphasized. The emergence of wearable imaging devices holds promise for sports rehabilitation and performance monitoring, enabling enhanced athlete health, recovery, and performance in the sports industry.

The cornerstone of textile manufacturing lies in quality control, with the early detection of defects being crucial to ensuring product quality and sustaining a competitive edge. Traditional inspection methods, which predominantly depend on manual processes, are limited by human error and scalability challenges. Recent advancements in artificial intelligence (AI)—encompassing computer vision, image processing, and machine learning—have transformed defect detection, delivering improved accuracy, speed, and reliability. This article critically examines the evolution of defect detection methods in the textile industry, transitioning from traditional manual inspections to AI-driven automated systems. It delves into the types of defects occurring at various production stages, assesses the strengths and weaknesses of conventional and automated approaches, and underscores the pivotal role of deep learning models, especially Convolutional Neural Networks (CNNs), in achieving high precision in defect identification. Additionally, the integration of cutting-edge technologies, such as high-resolution cameras and real-time monitoring systems, into quality control processes is explored, highlighting their contributions to sustainability and cost-effectiveness. By addressing the challenges and opportunities these advancements present, this study serves as a comprehensive resource for researchers and industry professionals seeking to harness AI in optimizing textile production and quality assurance amidst the ongoing digital transformation.

This research presents an approach aimed at enhancing texture recognition and weaving parameter estimation in the textile industry to align with sustainability goals and improve product quality. By utilizing low-cost handheld microscopy and machine learning, this method offers the potential for more precise production outcomes. In this study, textile images were manually labeled for texture, specific mass, weft, and warp parameters, followed by the extraction of various texture features, resulting in a comprehensive dataset comprising four hundred and fifty-eight inputs and four outputs. Prominent machine learning algorithms, including XGBoost, RF, and MLP, were applied, resulting in noteworthy achievements. Specifically, XGBoost demonstrated an impressive texture classification accuracy of 0.987, while RF yielded the lowest MAE (5.121 g/cm) in specific mass prediction. Additionally, weft and warp estimations displayed superior accuracy compared to manual measurements. This research emphasizes the crucial role of AI in improving efficiency and sustainability within the textile industry, potentially reducing resource wastage, enhancing worker safety, and increasing productivity. These advancements hold the promise of significant positive environmental and social impacts, marking a substantial step forward in the industry’s pursuit of its objectives.

Although textile production is heavily automation-based, it is viewed as a virgin area with regard to Industry 4.0. When the developments are integrated into the textile sector, efficiency is expected to increase. When data mining and machine learning studies are examined in textile sector, it is seen that there is a lack of data sharing related to production process in enterprises because of commercial concerns and confidentiality. In this study, a method is presented about how to simulate a production process and how to make regression from the time series data with machine learning. The simulation has been prepared for the annual production plan, and the corresponding faults based on the information received from textile glove enterprise and production data have been obtained. Data set has been applied to various machine learning methods within the scope of supervised learning to compare the learning performances. The errors that occur in the production process have been created using random parameters in the simulation. In order to verify the hypothesis that the errors may be forecast, various machine learning algorithms have been trained using data set in the form of time series. The variable showing the number of faulty products could be forecast very successfully. When forecasting the faulty product parameter, the random forest algorithm has demonstrated the highest success. As these error values have given high accuracy even in a simulation that works with uniformly distributed random parameters, highly accurate forecasts can be made in real-life applications as well.

SARS-CoV-2, the causative agent of COVID-19, which was officially declared a pandemic by the World Health Organization (WHO) on 11 March 2020, is transmitted from person to person through respiratory droplets and close contact and can cause severe respiratory failure and pneumonia. Currently, while the worldwide COVID-19 pandemic is still ongoing and countries are taking strict precautions to protect populations against infection, the most effective precautions still seem to be social distancing and wearing a mask. The question of how effective masks were in the early stages of the COVID-19 pandemic has been widely discussed, both in public and scientific circles, and the protection of different mask types has been examined. This study aimed to examine the comfort conditions provided by the different mask types to the user during use. For this purpose, single-ply, double-ply, three-ply, cloth, FFP1, FFP2, and FFP3 masks with different standards were examined, with and without a valve. To conduct the experiments, the novel thermal head measurement system, developed within the scope of this study, was used specifically for mask comfort studies. Thanks to the developed measurement system, the thermal resistance and water vapor resistance values of different masks were measured, and their comfort conditions were evaluated. According to the findings, cloth masks provide a comfortable condition, with lower thermal resistance and water vapor resistance values than other masks. In addition, it was observed that surgical masks offer better thermal comfort conditions, although they have lower protection than FFP masks.