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

TL;DR

This study explores autoencoder-based feature extraction for wearable sensor data, achieving significant storage reduction while maintaining data reconstructability for healthcare applications.

Contribution

It compares multiple autoencoder architectures for efficient data storage and reconstruction in human activity recognition, highlighting the effectiveness of MLP autoencoders.

Findings

MLP autoencoder reduced storage by 90.18%

Convolutional autoencoder achieved 11.18% reduction

Reconstructed data maintained high classification accuracy

Abstract

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.