Blood volume pulse (BVP) measurements obtained through the noninvasive photoplethysmography technique are often analyzed to derive physiological variables such as heart rate (HR) and HR variability, blood pressure, and respiratory rate. Physical activity is one of the predominant factors inducing changes in the BVP signal. The goal of the present study is to develop a mathematical model to characterize the exercise-induced changes in the BVP signal. A two-step procedure is developed that first models the HR using specified exercise intensity information, and then characterizes the change in the BVP waveform based on the estimated HR. The HR model parameters are identified using experiments involving 18 subjects participating in bouts of running and cycling exercise. Speed and incline for the treadmill and cycling power for the stationary bike are inputs to the HR model. The mean (standard deviation) for the root-mean-square error of the HR predictions are 16.1 (4.0) and 12.47 (4.6) BPM during running and cycling, respectively. The HR is transformed to the BVP signal through a model developed based on log-normal functions where model parameters change due to HR variability.