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.

利用商品耳机实时监测呼吸频道和阶段以评估呼吸疗法的遵守情况的潜力。