Classification of pulmonary breathing movements based on time-series imaging

Pulmonary rehabilitation training is one of the important therapeutic measures for patients with chronic obstructive pulmonary disease (COPD) during the clinical remission period. It aims to improve lung function through a series of respiratory muscle training exercises. Common respiratory muscle training includes pursed-lip breathing and diaphragmatic breathing, which can effectively improve lung ventilation efficiency. This paper innovatively applies RFID technology to pulmonary function breathing training, proposing the use of Gramian Angular Field (GAF) and Markov Transition Field (MTF) to convert one-dimensional RSSI breathing signal data into multi-channel image data, more comprehensively reflecting the dynamic changes and characteristics of breathing signals. By classifying respiratory muscle actions through the Mnasnet network model, and inspired by Mnasnet, we propose a Mnasnet-3Block-SVM network model with fewer parameters and shorter inference time. This model achieves a classification accuracy of 96.59% for respiratory actions, an improvement of 2.09% over the baseline model.