Adriana Arza Valdes

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SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices

David Atienza Alonso, Giulio Masinelli, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola

The photoplethysmographic (PPG) signal is an unobtrusive blood pulsewave measure that has recently gained popularity in the context of the Internet of Things. Even though it is commonly used for heart rate detection, it has been lately employed on multimod ...

2021

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

David Atienza Alonso, Amir Aminifar, Miguel Peon Quiros, Adriana Arza Valdes, Elisabetta De Giovanni

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 ...

2021

Self-Aware Machine Learning for Multimodal Workload Monitoring During Manual Labor on Edge Wearable Sensors

David Atienza Alonso, Amir Aminifar, Giulio Masinelli, Adriana Arza Valdes, Farnaz Forooghifar

The design of reliable wearable technologies for real-time and long-term monitoring presents a major challenge. Self-awareness is a promising solution that enables the system to monitor itself in interaction with the environment and to manage its resources ...

2020

Cognitive workload monitoring in virtual reality based rescue missions with drones

David Atienza Alonso, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Niloofar Momeni

The use of drones in search and rescue (SAR) missions can be very cognitively demanding. Since high levels of cognitive workload can negatively affect human performance, there is a risk of compromising the mission and leading to failure with catastrophic o ...

2020

EEG Correlates of Difficulty Levels in Dynamical Transitions of Simulated Flying and Mapping Tasks

David Atienza Alonso, José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Ping-Keng Jao

Decoding the subjective perception of task difficulty may help improve operator performance, i.e., automatically optimize the task difficulty level. Here, we aim to decode a compound of cognitive states that covaries with the task difficulty level. We desi ...

2020

Real-Time Cognitive Workload Monitoring Based on Machine Learning Using Physiological Signals in Rescue Missions

David Atienza Alonso, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Niloofar Momeni

High levels of cognitive workload decreases human's performance and leads to failures with catastrophic outcomes in risky missions. Today, reliable cognitive workload detection presents a common major challenge, since the workload is not directly observabl ...

IEEE2019

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

David Atienza Alonso, Jean-Marc Vesin, Sasan Yazdani, Lara Orlandic, Adriana Arza Valdes, Elisabetta De Giovanni

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 ...

2019

Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices

David Atienza Alonso, Amir Aminifar, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Victoriano Montesinos Canovas

Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this pap ...

IEEE2019

Measuring acute stress response through physiological signals: towards a quantitative assessment of stress

Adriana Arza Valdes

2019

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