In recent years, knitted strain sensors have been developed that aim to achieve reliable sensing and high wearability, but they are associated with difficulties due to high hysteresis and low gauge factor (GF) values. This study investigated the electromechanical performance of the weft-knitted strain sensors with a systematic approach to achieve reliable knitted sensors. For two elastic yarn types, six conductive yarns with different resistivities, the knitting density as well as the number of conductive courses were considered as variables in the study. We focused on the 1 × 1 rib structure and in the sensing areas co-knit the conductive and elastic yarns and observed that positioning the conductive yarns at the inside was crucial for obtaining sensors with low hysteresis values. We show that using this technique and varying the knitting density, linear sensors with a working range up to 40% with low hysteresis can be obtained. In addition, using this technique and varying the knitting density, linear sensors with a working range up to 40% strain, hysteresis values as low as 0.03, and GFs varying between 0 and 1.19 can be achieved.

As an emerging technology, smart textiles have attracted attention for rehabilitation purposes or to monitor heart rate, blood pressure, breathing rate, body posture, as well as limb movements. Traditional rigid sensors do not always provide the desired level of comfort, flexibility, and adaptability. To improve this, recent research focuses on the development of textile-based sensors. In this study, knitted strain sensors that are linear up to 40% strain with a sensitivity of 1.19 and a low hysteresis characteristic were integrated into different versions of wearable finger sensors for rehabilitation purposes. The results showed that the different finger sensor versions have accurate responses to different angles of the index finger at relaxation, 45° and 90°. Additionally, the effect of spacer layer thickness between the finger and sensor was investigated.

As an emerging technology, smart textiles have attracted attention for rehabilitation purposes to monitor heart rate, blood pressure, breathing rate, body posture and limb movements. Compared with traditional sensors, knitted sensors constructed from conductive yarns are breathable, stretchable and washable, and therefore, provide more comfort to the body and can be used in everyday life. In this study, knitted strain sensors were produced that are linear with up to 40% strain, sensitivity of 1.19 and hysteresis of 1.2% in absolute values, and hysteresis of 0.03 when scaled to the working range of 40%. The developed sensor was integrated into a wearable wrist-glove system for finger and wrist monitoring. The results show that the wearable was able to detect different finger angles and positions of the wrist.

Knitted heaters have attracted significant interest due to their flexibility and ease of integration into smart textile applications. However, uneven heat distribution remains a major challenge, leading to comfort issues and inefficient energy usage. This study presents an analytical, physics-based model that links the resistance, power distribution, and surface temperature of knitted heaters to key design parameters such as size, configuration, material properties, and knitting structure to establish guidelines for achieving a desired temperature rise over a specified surface area. The model was validated experimentally across a range of heaters (3–12 lines) arranged in ladder and diagonal configurations. Results showed good agreement between predictions and measurements for higher line counts (10–12), while larger deviations occurred in smaller heaters (3–5 lines) due to contact resistance and current losses. A prototype knitted wristband demonstrated physiologically relevant heating (>33 °C) under safe, low-voltage operation. These findings provide a quantitative design framework for optimizing knitted heaters and highlight their potential for scalable integration into wearable and therapeutic applications.