Related papers: Continuous User Authentication using IoT Wearable …
Smart wearable devices (SWDs) collect and store sensitive daily information of many people. Its primary method of identification is still the password unlocking method. However, several studies have shown serious security flaws in that…
Voice has become an increasingly popular User Interaction (UI) channel, mainly contributing to the ongoing trend of wearables, smart vehicles, and home automation systems. Voice assistants such as Siri, Google Now and Cortana, have become…
Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many…
An arrhythmia, also known as a dysrhythmia, refers to an irregular heartbeat. There are various types of arrhythmias that can originate from different areas of the heart, resulting in either a rapid, slow, or irregular heartbeat. An…
The classification of electrocardiogram (ECG) signals is crucial for early detection of arrhythmias and other cardiac conditions. However, despite advances in machine learning, many studies fail to follow standardization protocols, leading…
A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG).…
Verification of data generated by wearable sensors is increasingly becoming of concern to health service providers and insurance companies. There is a need for a verification framework that various authorities can request a verification…
The electrocardiogram (ECG) is a fundamental tool in cardiovascular diagnostics due to its powerful and non-invasive nature. One of the most critical usages is to determine whether more detailed examinations are necessary, with users…
Electrocardiogram (ECG) is one of the non-invasive and low-risk methods to monitor the condition of the human heart. Any abnormal pattern(s) in the ECG signal is an indicative measure of malfunctioning of the heart, termed as arrhythmia.…
The real time analysis and secure transmission of electrocardiogram (ECG) signals are critical for ensuring both effective medical diagnosis and patient data privacy. In this study, we developed a real time ECG monitoring system that…
The classification of the electrocardiogram (ECG) signal has a vital impact on identifying heart-related diseases. This can ensure the premature finding of heart disease and the proper selection of the patient's customized treatment.…
Authentication and authorization are critical security layers to protect a wide range of online systems, services and content. However, the increased prevalence of wearable and mobile devices, the expectations of a frictionless experience…
Nowadays, mobile smart devices are widely used in daily life. It is increasingly important to prevent malicious users from accessing private data, thus a secure and convenient authentication method is urgently needed. Compared with common…
Remote patient monitoring based on wearable single-lead electrocardiogram (ECG) devices has significant potential for enabling the early detection of heart disease, especially in combination with artificial intelligence (AI) approaches for…
We consider the problem of modeling cardiovascular responses to physical activity and sleep changes captured by wearable sensors in free living conditions. We use an attentional convolutional neural network to learn parsimonious signatures…
Atrial Fibrillation (AF) is a common cardiac arrhythmia affecting a large number of people around the world. If left undetected, it will develop into chronic disability or even early mortality. However, patients who have this problem can…
This paper presents a low power ECG recording Sys-tem-on-Chip (SoC) with on-chip low complexity lossless ECG compression for data reduction in wireless/ambulatory ECG sensor devices. The proposed algorithm uses a linear slope predictor to…
This study describes the creation of a wireless, transportable Holter monitor to improve the accuracy of cardiac disease diagnosis. The main goal of this study is to develop a low-cost cardiac screening system suited explicitly for…
Background and Objectives: This paper focuses on using AI to assess the cognitive function of older adults with mild cognitive impairment or mild dementia using physiological data provided by a wearable device. Cognitive screening tools are…
The 12-lead electrocardiogram (ECG) is a commonly used tool for detecting cardiac abnormalities such as atrial fibrillation, blocks, and irregular complexes. For the PhysioNet/CinC 2020 Challenge, we built an algorithm using gradient…