To better understand the interaction of physical activity and air pollution exposure, it is important to quantify the change in ventilation rate incurred by activity. In this paper, we describe a method for estimating ventilation using easily-measured variables such as heart rate (HR), breathing rate (fB), and forced vital capacity (FVC). We recruited healthy adolescents to use a treadmill while we continuously measured HR, fB, and the tidal volume (VT) of each breath. Participants began at rest then walked and ran at increasing speed until HR was 160-180 beats per minute followed by a cool down period. The novel feature of this method is that minute ventilation ([Formula: see text]) was normalized by FVC. We used general linear mixed models with a random effect for subject and identified nine potential predictor variables that influence either [Formula: see text] or FVC. We assessed predictive performance with a five-fold cross-validation procedure. We used a brute force selection process to identify the best performing models based on cross-validation percent error, the Akaike Information Criterion and the p-value of parameter estimates. We found a two-predictor model including HR and fB to have the best predictive performance ([Formula: see text]/FVC = -4.247+0.0595HR+0.226fB, mean percent error = 8.1±29%); however, given the ubiquity of HR measurements, a one-predictor model including HR may also be useful ([Formula: see text]/FVC = -3.859+0.101HR, mean percent error = 11.3±36%).