## 2 The Literature Review
This study aligns with previous research by emphasizing the importance of advanced materials, manufacturing processes, and technical solutions to ensure both functionality and comfort in sensor-based garments used for physiological monitoring. The study by [PERSON] et al. expands upon the existing research by investigating textile-based smart sensors, particularly Intrinsically Conductive Polymers (ICPs) and Elastomeric Conductive Polymers (ECPs), which offer mechanical adaptability to textile fabric [20]. These sensors provide lightweight, stretchable, and wearable properties, making them suitable for real-time and online monitoring of human health. The study aims to discuss recent developments in smart monitoring sensors, linking them to broader research on integrating textile technologies and sensorized garments for continuous physiological monitoring. [PERSON] et al. focused on how machine learning and neural networks offer efficient control and awareness of quality, enabling energy savings of up to 20% per machine [21]. Integrated sensors and real-time monitoring facilitate process parameter optimization for low energy consumption and high quality, empowering machine operators and production management with real-time information for improved decision-making and digital transformation. Several studies highlight the growing importance of sustainability considerations in the textile sector due to increasing environmental awareness and regulatory pressures. [PERSON] et al. explore how dynamic capabilities, such as sensing and seizing, can support sustainable strategic orientations in the apparel industry [22]. In line with the proposed topic, [PERSON] et al. presented an article that combines UAV (Unmanned Aerial Vehicle) multispectral and RGB (Red, Green, Blue) remote sensing and machine learning to model cotton fiber quality indicators, achieving improved prediction accuracy compared to traditional methods [23]. It provides a non-invasive and scalable approach for predicting cotton fiber quality, aiding variety breeding and commercial decision-making in the industry. Further advancements have been made in real-time quality monitoring and predictive analysis. [PERSON] et al. examine the impact of Industry 4.0 technologies on decision-making in the textile and clothing industry, highlighting trends such as data-driven, real-time, decentralized, integrated, and sustainable decision-making [24]. [PERSON] et al. considered the systematic literature review that explores the development process of Industry 4.0 in the textile and apparel sector, highlighting concepts and examples related to IoT, cloud computing, big data, 3D printing, augmented reality, and more [25]. A dynamic knowledge modeling and fusion method based on a knowledge graph is proposed by [PERSON] et al. for custom apparel production, addressing the challenges of data redundancy and weak correlations [26]. Their method enables effective knowledge fusion and representation, demonstrated through a case study on suit production in a custom apparel factory to enhance efficiency and quality control. The integration of IoT and remote sensing enables real-time monitoring and analysis of physical parameters, leading to improved process control, reduced waste, and enhanced product quality. The implementation of IoT in circular textile economy practices is crucial for promoting sustainability, resource efficiency, and real-time traceability throughout the textile value chain [27]. While the textile industry faces challenges in adopting smart manufacturing technologies, case studies demonstrate successful transitions to smart processes through IoT applications like RFID technology for material tracking and custom product manufacturing [28]. The implementation of real-time monitoring using IoT sensors in the Pakistani textile sector can significantly enhance operational efficiency and sustainability [29].