Deep Convolutional Autoencoder for Energy-efficient Smart Health Wearables In the Era of Big Data

Abstract

Smart wearables, known as emerging paradigms for vital big data capturing, have been attracting intensive attention. However, one crucial problem is their power-hungriness, i.e., the continuous data streaming consumes energy dramatically and requires devices to be frequently charged. In this study, targeting this obstacle, we propose to investigate the biodynamic patterns in the data, and design a data-driven approach for intelligent data compression. We leverage deep learning, more specifically, Convolutional Autoencoder (CAE), to learn a sparse representation of the vital big data, thereby minimizing the power consumption of the communication module – the most power-hungry module in the wearable. The minimized energy need, even taking into consideration the CAE-induced overhead, is tremendously lower than the original energy need. Further, compared with state-of-the-art wavelet compression-based method, our method can compress the data with a dramatically lower error for a similar energy budget. Our experiments and the validated approach, are expected to boost the energy efficiency of wearables, and thus greatly advance ubiquitous big data applications in the era of smart health.