Variational AutoEncoders for Biosensor Data Augmentation

Over the past decade machine learning and artificial intelligence’s resurgence spawned the desire to mimic human creative ability. Initially attempts to create images, music, and text flooded the community, though little has been learned regarding constrained, one-dimensional data generation. This paper demonstrates a variational autoencoder approach to this problem. By modeling biosensor current and concentration data we aim to augment the existing dataset. In training a multi-layer neural network based encoder and decoder we were able to generate realistic, original samples. These results demonstrate the ability to realistically augment datasets, improving training of machine learning models designed to predict concentration from input signals.

https://digitalcommons.andrews.edu/honors-undergraduate-poster-symposium/2021/symposium/18