audio based machine learning models to infer pulmonary health, exacerbation and activity. A major challenge to  widespread  usage  and  deployment of such pulmonary health monitoring audio models is to maintain accuracy  and  robustness  across  a  variety  of  commodity devices, due to the effect of device heterogeneity. Because of this phenomenon, pulmonary audio models developed with data from one type of device perform poorly when deployed on another type of device. In this work, we propose a framework incorporating a multivariate deep neural network regressor as a feature translator from the source device domain to the target device domain. Our empirical and extensive experiments with data from 131 real pulmonary patients and healthy controls show that our framework can recover upto 66.67%  of the accuracy lost due to device heterogeneity for two different pulmonary activity based person identification tasks with two common mobile and wearable devices: smartphone and smartwatch.

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9871967