An efficient data compression algorithm for real-time monitoring applications in healthcare

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wireless sensor technology has revolutionised healthcare practices to deal with the increasing number of chronically ill patients. Real-time and continuous monitoring of health parameters can help in early diagnosis and timely treatment. Sensor nodes having limited resources in health monitoring systems are equipped with number of sensors which generates huge amount of data. An increase in data results in an increase in power consumption and memory requirement. An efficient data compression algorithm can be applied to reduce the power consumption and memory requirement. Minimalist, Adaptive and Streaming (MAS) algorithm proposed in literature can reduce significant power consumption during data transmission. In current work, MAS algorithm is further optimised to propose O-MAS-R algorithm by introducing R-bit to take advantage of consecutive repetition of data samples. MAS and O-MAS-R algorithms are applied on Electrocardiography (ECG), Electromyography (EMG) and accelerometer (Acc) datasets to compare the performance in terms of compression ratio (CR). O-MAS-R has shown 7.21 % average increase in CR of ECG datasets, 8.25% increase in EMG datasets and 45.24% increase in Acc datasets as compare to MAS algorithm.

Original languageEnglish
Title of host publication2020 5th International Conference on Computer and Communication Systems, ICCCS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages71-75
Number of pages5
ISBN (Electronic)9781728161365
DOIs
Publication statusPublished - 16 Jun 2020
Event5th International Conference on Computer and Communication Systems - Shanghai, China
Duration: 15 May 202018 May 2020

Publication series

Name2020 5th International Conference on Computer and Communication Systems, ICCCS 2020

Conference

Conference5th International Conference on Computer and Communication Systems
Abbreviated titleICCCS 2020
CountryChina
CityShanghai
Period15/05/2018/05/20

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