Knitted Sensors Research Articles

Continuous Wet-Spinning of TPU Composite Fibers for Wearable Knitted Sensors

Continuous Wet-Spinning of TPU Composite Fibers for Wearable Knitted Sensors

Investigation of flexible graphene hybrid knitted sensor for joint motion recognition based on convolutional neural network fusion long short-term memory network

Wearable electronics have attracted have attracted widespread attentions for their promising applications in motion monitoring and human-computer interaction. This paper proposes a flexible wearable joint movement intelligent sensing and recognition system to achieve stable and reliable motion feature extraction and recognition. Flexible graphene hybrid knitted sensor were prepared by transferring graphenes (GNs) agent onto stretchable knitted products via a simple spray-drying approach. The small dynamic movement of human joints for the prepared GNs hybrid sensing gloves, elbow pads and knee pads were converted into electrical signals for sensitive detection. The convolutional neural network fusion long short-term memory (CNN-LSTM) network with self-attention mechanism (SAM) is established for feature training and intelligent dynamic recognition of the measured joint information. The interconnected conductive networks endowed knitted sensor with good flexibility and remarkable electrical conductivity of 37 S/m. The unique conductive networks in the fabric offered excellent linearity and repeatable resistance response variation for better detection of joint motion. The resistant signal was analyzed by feature extraction, data correlation capture and time sequence relationship modeling. Finally, the test results show that the proposed CNN-LSTM with SAM network achieves 97%, 96% and 100% correct recognition rates for gesture signals, elbow and wrist signals and knee signals respectively, which is obviously higher than other recognition algorithms. It has great application prospects in the fields of smart wear, medical detection, and smart elderly care.

Design, preparation, and performance research of a knitted sensor for respiratory rate detection

Owing to flexible, good wearing comfort and fitting the human body well, flexible knitted sensors can play a significant role in monitoring respiratory signals, an important auxiliary diagnostic information. We proposed an idea that underwear is equipped with flexible sensors to realize respiratory signal detection by daily underwear. Four conductive fibers were used to knit jersey stitches and ribs to produce eight knitted flexible sensor samples. We explored flexible knitted sensors' design and preparation first. Considering the influence of production and daily cleaning on durability, we tested conductive properties, an essential property for sensors before and after heat setting and multi-wash tests. Meanwhile, practical application was tested. The results prove that the most suitable raw materials and knitting structure are 111dtex silver-plated yarn and jersey stitch. Because the sensor fabricated by 111dtex silver-plated yarn has better durability. Washing can optimize its conductivity and the conductivity of sensors fabricated by other conductive yarn is worse than that after washing. Then, the amounts of courses can influence conductivity greatly. Under the same condition, more courses mean better conductivity and durability after washing. Meanwhile, the heat setting temperature can be selected as 100–120 ℃. Because this temperature can promote the conductive properties of most sensors. At last, practical applications prove the feasibility of sensors. There is only a slight gap between the tested by sensors and the actual value. This paper can guide the design of flexible knitting sensors for raw materials and structure. What’s more, it is meaningful for the thermal setting process in the actual production process of the factory. Last but not least, the underwear equipped with sensors based on integrated weaving can detect respiratory frequency accurately. This will make a significant difference in medicine.

Development of Low Hysteresis, Linear Weft-Knitted Strain Sensors for Smart Textile Applications.

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.

Effects of Base Fabric Parameters on the Electro-Mechanical Behavior of Piezoresistive Knitted Sensors

Strain sensors embedded into fabric structure are one of the most interesting research areas for health and engineering monitoring. In this paper, a textile-based strain sensor has been developed using an intrinsically conductive stainless steel-polyester plain knitted fabric. The influence of fabric structural parameters, such as loop length on electro-mechanical properties of sensors was studied under a tensile fatigue set. Knitted fabric structure deformation during tensile fatigue was monitored and image processed. Peak fitting of electric resistant waveforms (ERWs) and tensile fatigue versus time were performed to analyze the ERWs behavior in detail wherein the entire elongation of knit strain sensor partitioned into two distinct phases, such as loop head and loop leg sections. The results evidenced a discrepancy between loop head and loop leg elongation rate at the stretching onset and end of relaxing phase. The ERW with the higher resolution was obtained using the longest loop length along with the higher extension.

Using Knitted Sensor Technology to Identify Gait Events during Walking

Using Knitted Sensor Technology to Identify Gait Events during Walking