  Several therapy routines require deep breathing exercises as a key component
and patients undergoing such therapies must perform these exercises regularly.
Assessing the outcome of a therapy and tailoring its course necessitates
monitoring a patient's compliance with the therapy. While therapy compliance
monitoring is routine in a clinical environment, it is challenging to do in an
at-home setting. This is so because a home setting lacks access to specialized
equipment and skilled professionals needed to effectively monitor the
performance of a therapy routine by a patient. For some types of therapies,
these challenges can be addressed with the use of consumer-grade hardware, such
as earphones and smartphones, as practical solutions. To accurately monitor
breathing exercises using wireless earphones, this paper proposes a framework
that has the potential for assessing a patient's compliance with an at-home
therapy. The proposed system performs real-time detection of breathing phases
and channels with high accuracy by processing a $\mathbf{500}$ ms audio signal
through two convolutional neural networks. The first network, called a channel
classifier, distinguishes between nasal and oral breathing, and a pause. The
second network, called a phase classifier, determines whether the audio segment
is from inhalation or exhalation. According to $k$-fold cross-validation, the
channel and phase classifiers achieved a maximum F1 score of $\mathbf{97.99\%}$
and $\mathbf{89.46\%}$, respectively. The results demonstrate the potential of
using commodity earphones for real-time breathing channel and phase detection
for breathing therapy compliance monitoring.
