Performance evaluation shows that HRV is a strong candidate for continuous unobtrusive implicit physiological authentication. By applying a minimum quality threshold, we achieved a 3.96% Equal Error Rate. In order to train our machine learning models, we collected physiological data using off-the-shelf smartbands and smartwatches in a real-life event. After the artifact removal, cleaned physiological signals are fed to the machine learning algorithms.
A system that is suitable for the smartband framework comes with new challenges such as relatively low signal quality and artifacts due to placement, which were not encountered in full lead electrocardiogram systems. In this study, we show that these devices can be used for continuous physiological authentication for enhancing the security of the cloud, edge services, and IoT devices.
Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors, which enable us to extract heart rate variability (HRV) features.
Physiological signals can be used for continuous authentication due to their uniqueness. The use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution, which will make these systems less vulnerable to the spoofing attacks. Typical data collected is presented and analyzed using Poincare plot and Rescale Range analysis technique and thus fractal dimension of the time series of HR signal is determined and details discussed. Here we present an inexpensive AVR microcontroller based data acquisition system with computer interface. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Hence t is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. The indicators may be present at all times or may occur at random during certain intervals of the day. Heart Rate (HR) is a non stationary signal its variation may contain indicators of current disease, or warnings about impending cardiac diseases. In fact, this provides a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. Heart rate variability (HRV) is a reliable index and reflection of the many physiological factors modulating the normal rhythm of the heart.
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