Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction

ENAMAMU, Timibloudi, OTEBOLAKU, Abayomi, MARCHANG, Jims and DANY, Joy (2020). Continuous m-Health Data Authentication Using Wavelet Decomposition for Feature Extraction. Sensors, 20 (19): 5690.

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Abstract

The World Health Organization (WHO) in 2016 considered m-health as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health”. WHO emphasizes the potential of this technology to increase its use in accessing health information and services as well as promoting positive changes in health behaviours and overall management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring through the collection of patient data remotely, has become an important component in m-health system. It is important that the integrity of the data collected is verified continuously through data authentication before storage. In this research work, we extracted heart rate variability (HRV) and decomposed the signals into sub-bands of detail and approximation coefficients. A comparison analysis is done after the classification of the extracted features to select the best sub-bands. An architectural framework and a used case for m-health data authentication is carried out using two sub-bands with the best performance from the HRV decomposition using 30 subjects’ data. The best sub-band achieved an equal error rate (EER) of 12.42%.

Item Type:	Article
Uncontrolled Keywords:	Analytical Chemistry; 0301 Analytical Chemistry; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; 0502 Environmental Science and Management; 0602 Ecology
Identification Number:	https://doi.org/10.3390/s20195690
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	07 Oct 2020 13:30
Last Modified:	11 Oct 2023 13:00
URI:	https://shura.shu.ac.uk/id/eprint/27365

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