Hybrid Deep Learning Model Assisted Data Compression and Classification for Efficient Data Delivery in Mobile Health Applications

With the growing amount of chronic patients, consistent monitoring for health care professionals has been a major concern and a direct incentive to develop mobile health systems that are adaptive and energy-efficient. The data collected from these devices is extremely important and may be affected by wireless communication environments encouraging a preliminary stage that adapts transmission of data to network dynamics. The paper provides compression and classification schemes for data based on a Hybrid Deep Learning Model (HDLM) that represents data characteristics, acquired data, and energy efficiency data delivery dynamics. Further, the EEG and EMG signals are compressed and classified based on Hybrid Deep Learning Model (HDLM) has been mathematically analyzed. Hence, The system is specifically based on the Stacked Auto-Encoder (SAE) architecture which extracts discrimination in the multimodal representation of data; it reconstructs data from the latent description with the help of encoder-decoder layers for data analysis. Furthermore, Multi-Modality Adaptive Compression shows its performance, computational complexity and response to different network states has been experimentally analyzed at lab scale numerical analysis. This method is therefore appropriate for mHealth applications, which can improve energy efficiency, minimize capacity, and minimize transmission latency in the mHealth cloud with intelligent preprocessing.

Abstract licence: CC BY-NC-ND

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