  Breathing rate (BR), minute ventilation (VE), and other respiratory
parameters are essential for real-time patient monitoring in many acute health
conditions, such as asthma. The clinical standard for measuring respiration,
namely Spirometry, is hardly suitable for continuous use. Wearables can track
many physiological signals, like ECG and motion, yet not respiration. Deriving
respiration from other modalities has become an area of active research. In
this work, we infer respiratory parameters from wearable ECG and wrist motion
signals. We propose a modular and generalizable classification-regression
pipeline to utilize available context information, such as physical activity,
in learning context-conditioned inference models. Morphological and power
domain novel features from the wearable ECG are extracted to use with these
models. Exploratory feature selection methods are incorporated in this pipeline
to discover application-specific interpretable biomarkers. Using data from 15
subjects, we evaluate two implementations of the proposed pipeline: for
inferring BR and VE. Each implementation compares generalized linear model,
random forest, support vector machine, Gaussian process regression, and
neighborhood component analysis as contextual regression models. Permutation,
regularization, and relevance determination methods are used to rank the ECG
features to identify robust ECG biomarkers across models and activities. This
work demonstrates the potential of wearable sensors not only in continuous
monitoring, but also in designing biomarker-driven preventive measures.
