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In recent years, population aging and the consequent higher incidence of noncommunicable diseases, have increased the need for long-term health monitoring. Moreover, as healthcare cost is projected to grow substantially by 2030 in OECD countries, the deman ...

In the last years, the need for enhancing health and preventing problems with remote monitoring is increasing. A non-invasive low-cost technique for processing bio-signals and monitoring vital parameters, at rest and during physical activity, is the use of ...

In the recent Internet-of-Things (IoT) era where biomedical applications require continuous monitoring of relevant data, edge computing keeps gaining more and more importance. These new architectures for edge computing include multi-core and parallel compu ...

Atrial Fibrillation (AF) is a type of cardiac arrhythmia that significantly increases the risk of stroke and heart failure. In general, in the case of patients affected by AF, their electrocardiogram (ECG) shows a typical pattern of irregular RR intervals ...

In spite of the progress in management of Atrial Fibrillation (AF), this arrhythmia is one of the major causes of stroke and heart failure. The progression of this pathology from a silent paroxysmal form (PAF) into a sustained AF can be prevented by predic ...

Objective: Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is generally accurate. However, in conditions such as high-intensity exercise, sud ...

In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable senso ...

Wearable devices are an unobtrusive, cost-effective means of continuous ambulatory monitoring of chronic cardiovascular diseases. However, on these resource-constrained systems, electrocardiogram (ECG) processing algorithms must consume minimal power and m ...

Elisabetta De Giovanni

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System-level Design of Adaptive Wearable Sensors for Health and Wellness Monitoring

Ultra-Low Power Estimation of Heart Rate Under Physical Activity Using a Wearable Photoplethysmographic System

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

Hierarchical Cardiac-Rhythm Classification Based on Electrocardiogram Morphology

A Patient-Specific Methodology for Prediction of Paroxysmal Atrial Fibrillation Onset

Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise

Modular Design and Optimization of Biomedical Applications for Ultra-Low Power Heterogeneous Platforms

REWARD: Design, Optimization, and Evaluation of a Real-Time Relative-Energy Wearable R-Peak Detection Algorithm *