The purpose of the study is to collect data: audio in the form of respiratory sounds and instructions from the therapist, spirometry data during the execution of the therapy, physiological parameters, and patient information. With the help of this data, algorithms will be developed for the automatic detection, localization, and classification of respiratory sounds, linked to the respiratory cycle using spirometric signals.

The study is an interventional study. Patients receive their standard respiratory physiotherapy, but in the mean time they are monitored with a stethoscope, spirometer and a wearable to measure physio-logical parameters.

It has been chosen to intervene as little as possible in the therapy to measure the respiratory sounds and breathing of the patient as accurately as possible. By recording therapy sessions with 15 patients, we expect to register several hundred events of respiratory sounds. This provides a good foundation for training an algorithm to detect and classify respiratory sounds.

Inclusion Criteria:

• The patient has a chronic or acute respiratory condition and may need oxygen therapy
• The patient is minimally 12 years old

Exclusion Criteria:

• The patient is cognitively impaired.
• The patient has an undrained pneumothorax.
• The patient is experiencing an acute pulmonary hemorrhage.
• The patient is having an asthma exacerbation.
• The patient has an infection with Burkholderia cepatia.
• The patient is in isolation due to the risk of infection.