A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data

Efficient querying and retrieval on healthcare data is a critical challenge that we are facing today as connected devices are 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 the 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%, where as convolutional autoencoder achieved 11.18%. For Long-Short Term Memory (LSTM) autoencoder the reduction was 91.47% and for convolutional LSTM autoencoder it was 72.35%. The storage reduction depended on the size and dimension of the concise representation. For higher dimensions of the representation, the storage reduction was low. But relevant information retention was high which was validated by the classification performed on the reconstructed data.