Design and Implementation of Hybrid Compression Algorithm for Personal Health Care Big Data Applications

Advanced telemedicine requires the gathering of big data through wireless body area network or internet of things based applications. These networks perform several tasks that consume maximum energy while transmitting the big data, which in turn discharges the battery and would require a battery replacement frequently. Also, the big data to be transferred and stored would require a significant amount of storage space. The above problem is rectified by compressing the big data acquired from the sensors before transmission that would reduce the consumption of power as well as use the storage space efficiently. In this paper, a hybrid compression algorithm (HCA) based on Rice Golomb Coding is proposed. The efficiency of the proposed compression algorithm is tested on ECG data from the physionet ATM database and real-time data acquired from the ECG sensor. The proposed HCA comprises of both lossy and lossless compression. The real-time implementation of the proposed compression algorithm is carried out using NI myRIO hardware and LabVIEW graphical tool. The compressed data then stored in the Google cloud, and the analysis of storage space using the HCA shows a reduction of 70% storage space for 10 minutes of ECG data.