  Lung cancer is the number one cause of cancer deaths. Many early stage lung
cancer patients have resectable tumors; however, their cardiopulmonary function
needs to be properly evaluated before they are deemed operative candidates.
Consequently, a subset of such patients is asked to undergo standard pulmonary
function tests, such as cardiopulmonary exercise tests (CPET) or stair climbs,
to have their pulmonary function evaluated. The standard tests are expensive,
labor intensive, and sometimes ineffective due to co-morbidities, such as
limited mobility. Recovering patients would benefit greatly from a device that
can be worn at home, is simple to use, and is relatively inexpensive. Using
advances in information technology, the goal is to design a continuous,
inexpensive, mobile and patient-centric mechanism for evaluation of a patient's
pulmonary function. A light mobile mask is designed, fitted with CO2, O2, flow
volume, and accelerometer sensors and tested on 18 subjects performing 15
minute exercises. The data collected from the device is stored in a cloud
service and machine learning algorithms are used to train and predict a user's
activity .Several classification techniques are compared - K Nearest Neighbor,
Random Forest, Support Vector Machine, Artificial Neural Network, and Naive
Bayes. One useful area of interest involves comparing a patient's predicted
activity levels, especially using only breath data, to that of a normal
person's, using the classification models.
