This application provides a method,
system, and device for ventilator
mask recognition based on transfer learning. The method collects time-
series data of
respiratory flow and pressure using flow and pressure sensors inside a home ventilator; a data preprocessing module cleans the time-
series data to obtain high-quality
respiratory flow and pressure time-
series data; a data segmentation module divides the preprocessed time-series data into discrete flow and
pressure data segments; a transfer learning module transfers the parameters of the
source model to the target model, and the target model is trained and fine-tuned using the segmented discrete flow and
pressure data segments to obtain the final
mask type recognition model. The final
mask type recognition model can accurately predict the ventilator mask type based on
respiratory flow and pressure signals. This application utilizes transfer learning to improve the robustness of the model to mask type recognition across individuals and devices.