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%.
Highlights
The use of smartphones has increased over the years with many services adapting to mobile applications
The feature variations arewavelet important in choosing the mostThe effective features to apply on with MATLAB using the first level detail coefficient of biorthogonal wavelet transform sub-band
This paper investigated the authentication of m-health data for remote health care monitoring
Summary
The use of smartphones has increased over the years with many services adapting to mobile applications. The growth has seen competition in the use of mobile applications from marketing and advertising to goods and services. This has increased investment in the provision of services using apps on mobile devices. This is because it is more convenient to access the services on mobile devices compared to traditional computing sets. Knowledge based authentication mechanisms have been a traditional way for authenticating a user’s access to a device. The use of knowledge-based authentication to secure mobile devices has been effective but has its own limitations because the data used is static in nature. The use of secret information known to the user can be forgotten or can be obtained by another person if written down [3] or someone can brute force especially when the passwords are short, or default settings are not Sensors 2020, 20, 5690; doi:10.3390/s20195690 www.mdpi.com/journal/sensors
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