Deep learning enabled classification of real-time respiration signals acquired by MoSSe quantum dot-based flexible sensors.

Respiration rate is a vital parameter which is useful for the earlier identification of diseases. In this context, various types of devices have been fabricated and developed to monitor different breath rates. However, the disposability and biocompatibility of such sensors and the poor classification of different breath rates from sensor data are significant issues in medical services.