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.

Intelligent prediction of key output variables that are difficult to measure online in complex systems has important research significance. In this paper, by using the least absolute shrinkage and selection operator (LASSO) algorithm to analyze the principal elements of input variables, a temporal convolutional network fused with long short-term memory (TCN-LSTM) network and self-attention mechanism (SAM) is designed to realize dynamic modeling of multivariate feature sequences. For complex processes with multiple input variables, each variable has different effects on the output, so it is necessary to use the LASSO algorithm to perform regression analysis on the input and output data for selecting the principal component variables and reducing the redundancy and computation burden of the network. The TCN network is used to extract the features of the input variables efficiently. The long-term memory performance of time series is enhanced by applying an LSTM network. The multihead SAM is used to optimize the network, and the role of key features is enhanced by assigning weights with probability to further improve the accuracy of sequence prediction. Finally, by comparison with the existing network model, the offline data generated by the high and low converters in the synthetic ammonia industry is used to predict the CO content so as to verify the superiority and applicability of the proposed network model.

In addition to sportswear and outdoor equipment, moisture-absorbent quick-drying fabrics are also widely used in everyday clothing and home textiles. In this study, three types of weft-knitted fabrics were designed using Coolmax fiber and polypropylene fiber. The Coolmax/PP fabric exhibits good stretchability with a strain of 180.5% and achieves a high cumulative individual transfer capability of 691.6%, with a water absorption rate of 50.2%/s. The moisture conductivity gradient presented good moisture and heat conductivity in a simulated human body temperature environment using an infrared camera. Furthermore, mathematical modeling was constructed and visual simulation analysis was conducted to explore moisture–thermal transfer behavior. The simulation results closely align with experimental data, providing insights into designing flexible and wearable quick-drying fabrics for thermal management.