  Generative models such as the variational autoencoder (VAE) and the
generative adversarial networks (GAN) have proven to be incredibly powerful for
the generation of synthetic data that preserves statistical properties and
utility of real-world datasets, especially in the context of image and natural
language text. Nevertheless, until now, there has no successful demonstration
of how to apply either method for generating useful physiological sensory data.
The state-of-the-art techniques in this context have achieved only limited
success. We present PHYSIOGAN, a generative model to produce high fidelity
synthetic physiological sensor data readings. PHYSIOGAN consists of an encoder,
decoder, and a discriminator. We evaluate PHYSIOGAN against the
state-of-the-art techniques using two different real-world datasets: ECG
classification and activity recognition from motion sensors datasets. We
compare PHYSIOGAN to the baseline models not only the accuracy of class
conditional generation but also the sample diversity and sample novelty of the
synthetic datasets. We prove that PHYSIOGAN generates samples with higher
utility than other generative models by showing that classification models
trained on only synthetic data generated by PHYSIOGAN have only 10% and 20%
decrease in their classification accuracy relative to classification models
trained on the real data. Furthermore, we demonstrate the use of PHYSIOGAN for
sensor data imputation in creating plausible results.
