Deep‐Learning‐Based Prediction of Long‐Term Piezoresistive Sensing Performance of MXene/Aramid Nanofiber Sensors

Flexible compressible sensors are widely used in the human health monitoring field for their numerous advantages. However, the dynamic loads and possible injuries associated with long‐term living and exercise pose a challenge to the long‐term piezoresistive performance stability of these sensors. In this study, the application of deep learning for predicting the long‐term performance of these sensors is explored, aiming to enhance the assessment of sensor stability and ensure accurate and reliable long‐term monitoring. Samples with different Ti ₃ C ₂ T _x MXene/aramid nanofiber mass ratios (1:1, 1:2, 1:3) are prepared and piezoresistive characterization is conducted under long‐term loading cycles to obtain training data. Three distinct deep‐learning prediction models, convolutional neural network (CNN), long short‐term memory, and recurrent neural network (RNN), are utilized to assess their influence on prediction accuracy. To assess the effectiveness of the proposed method, its prediction of long‐term piezoresistive sensing performance with experimental data not used for training purposes is compared. The CNN model demonstrates optimal results with a mean absolute error of 0.0251 for the 1:3 mass ratio sample. Based on the experimental results, the model is expected to be integrated into human health monitoring systems, thus improving the assessment of sensor stability throughout its lifetime.