  Efficient querying and retrieval of healthcare data is posing a critical
challenge today with numerous connected devices continuously generating
petabytes of images, text, and internet of things (IoT) sensor data. One
approach to efficiently store the healthcare data is to extract the relevant
and representative features and store only those features instead of the
continuous streaming data. However, it raises a question as to the amount of
information content we can retain from the data and if we can reconstruct the
pseudo-original data when needed. By facilitating relevant and representative
feature extraction, storage and reconstruction of near original pattern, we aim
to address some of the challenges faced by the explosion of the streaming data.
We present a preliminary study, where we explored multiple autoencoders for
concise feature extraction and reconstruction for human activity recognition
(HAR) sensor data. Our Multi-Layer Perceptron (MLP) deep autoencoder achieved a
storage reduction of 90.18% compared to the three other implemented
autoencoders namely convolutional autoencoder, Long-Short Term Memory (LSTM)
autoencoder, and convolutional LSTM autoencoder which achieved storage
reductions of 11.18%, 49.99%, and 72.35% respectively. Encoded features from
the autoencoders have smaller size and dimensions which help to reduce the
storage space. For higher dimensions of the representation, storage reduction
was low. But retention of relevant information was high, which was validated by
classification performed on the reconstructed data.
