This study is to investigate to what extent the performance of compression stockings with cotton components deteriorates after repeated washing processes. Four compression stockings having at least one cotton constituent yarn and two all-nylon stockings as reference samples were produced under controlled commercial conditions. After analysing the data obtained, a care labelling process for the compression socks with cotton components was developed such that they can preserve their compression properties over successive laundering treatments.

Remote monitoring of a patient’s physical rehabilitation process after knee surgery is crucial, especially in instances such as pandemics, where patients may not be able to acquire ongoing postoperative care owing to the precautions implemented. Wearable technology can be used to track a patient’s development and ensure that they follow rehabilitation guidelines. An active rotation control (ARC) knee brace was developed to guide and facilitate physical therapy movements of patients with knee injuries in an actively controlled manner. The system can trigger a visual feedback mechanism when the subject performs various knee postures. Through the Internet, caregivers could obtain patients’ overall knee-related rehabilitation metrics. The ARC knee brace employs inertial motion tracking technology which is based on low-cost inertial sensors and data processing algorithms to capture user’s knee posture in real-time during rehabilitation process. The inertial measurement units (IMUs) containing a combination of accelerometer and gyroscope are used as motion sensors to measure accelerations and rotational rates of knee. The sensors track data include acceleration, rotation, and temperature. The processing system calculates various metrics from the posture of knee.

The textile industry is experiencing a fundamental transformation driven by the integration of artificial intelligence, digital twin technology, and intelligent control systems. This chapter examines how these technologies are changing textile engineering in practice from demand forecasting and design tools to digital twins on the factory floor and adaptive control systems that learn from production data. The discussion draws on machine learning, computer vision, and IoT (internet of things) sensing research to show where data-driven methods are beginning to outperform the trial and error approaches that textile engineers have relied on for decades. Particular attention is given to sustainability applications, including AI (artificial intelligence)-enabled resource optimization in dyeing and finishing, automated textile waste sorting for circular economy models, and digital product passports for life cycle transparency. The chapter critically evaluates both the disruptive potential and the practical challenges of these technologies, including data quality, implementation cost, workforce readiness, and model interpretability, providing textile engineers, researchers, and industry practitioners with a balanced and technically grounded perspective on the digital future of the discipline.