Abstract
Accurate data traffic models are crucial to characterize data flow and understand the network traffic behavior, playing paramount importance to network design and management. This letter presents an autoregression (AR) model to depict photoplethysmogram (PPG) signal-based traffic produced by wearable devices. We validate our model through empirical data using three public datasets and one local dataset. Results show that our AR model reproduces the pattern behavior, trend, and statistical characteristics of the original PPG signal. Also, the generated PPG samples have similar periodic autocorrelation peaks than the original ones, even under an error rate of 14%.
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Published Version
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